List of working experiences, education, and skills across my personal professional career

• type: fields
  desc: "Hands-on experience in data products and infrastructure, fostering cross-functional collaboration across
  technical, product, and commercial teams. Focused on data-driven decision-making built upon a robust
  background in statistical analysis, predictive modeling, algorithm design, machine learning, and data/text
  mining. Successful leadership of complex technical projects in both B2B and B2C environments, proficiently
  managed remote teams, and actively contributing to talent acquisition and mentorship."
• type: skills
  desc: "Specialist in optimization problems, productization of ML, prototyping (PoC, MVP), tech presales, personas
  segmentation, multichannel strategies and driving conversion uplift. 15+ years of coding experience in data
  proc/viz, cloud/grid computing, web, data platforms, ETL, front/back end."
• type: personal
  desc: "I developed a strong understanding of customer needs and learnt how to build durable customer stakeholding.
  Driven by innovation, novel business models and collaborative working environments. Passionate about story
  telling and crafting, good knowledge of international languages and long coaching experience."

• id: 9h
  title: Energy management
  desc: Data driven solutions can improve the efficiency of factories, especially
  regarding ventilation and heating. Here with the help of data we proposed 10 actions
  which can bring 10%-20% savings in energy consumption.
  story: We already had a successful case with the client in a multi milion project with 300% return. That project was on a specific stage of the production line where controlling HVAC system was crucial for a proper painting job. The project required robotics and data science together building digital twins to simulate and train the system to allow the experimentation of different regimes. In this case we had to provide an implementation plan to a big modern factory. Modern in the sense of advanced but old in terms of energy efficiency. When the factory was planed few years ago energy cost wasn't a topic and energy efficiency neither. We then had to visit the factory and check the ventilation and heating flow, from the cellar to the roof. The factory has big pumps, heater exchanger, roof ventilators and large pipes for warm and cold water circulation.
  kpi: 10%-20% energy cost improvement
  customer: 'DE: automotive'
  industry: automotive
  duration: 3m
  role: Analytics consultant
  team: 5.0
  responsibilities: Conduct the client visit, visit the factory and the data room.
  Talk with all stakeholders and design an action plan for energy efficiency
  tech: skelarn, PLC, scada
  label: energy, efficiency
  year: 2025
• id: 9g
  title: First level support bot
  desc: Managing a fleet requires a stable infrastructure and a prompt resolution
  of issues. End clients sometimes have issues in using the portal and first level
  support can be simplified by agents.
  customer: 'DE: automotive'
  industry: automotive
  duration: 1m
  role: Tech consultant
  kpi: increase response time, error resolution and customer satisfaction
  team: 1.0
  responsibilities: Conduct the client visit, design the implementation plan, create
  the mockup for the client to test
  tech: d3, llm, python
  label: automation
  year: 2025
• id: 9f
  title: Pipeline operations
  desc: Managing a large pipeline infrastructure need 24/7 human support to be able
  to act in case some problem arise
  customer: 'DE: energy'
  industry: energy
  duration: 3m
  role: Data scientist
  kpi: Increase accuracy, system stability and predictive maintenance
  team: 3.0
  responsibilities: Collect all the data for the Bavarian gas subnet, create the graph,
  map the quantities to the respective nodes, run simulations, bechmark ML results
  tech: python, skleran
  label: energy, efficiency
  year: 2024
• id: 9i
  title: RFP for the public sector.
  desc: Government offices and public utilities need important modernization on their digital environment. Processes are not traceable and interfaces lack capabilities to connect with modern tools like agents.
  customer: 'DE: public'
  industry: public
  duration: 1y
  role: Tech consultant
  kpi: Precision of solution and requirements for better RFP acceptance
  team: 10
  responsibilities: Collect all the requirements and prepare the specific questions regarding the client tech stack and needs. Prepare an implementation plan following the guide line of the RFP and explain how the execution would be enrolled for the length of the project, usually across multiple year.
  tech: cloud and on-prem computing
  label: public, digitalization
  year: 2024-2006
• id: 9e
  title: Agentic system
  desc: A complex manifacturing corporate need a strategy to design a data platform
  to enable agents to support their workforce. We consult the client in designing
  a platform that enable the integration and cosinstency of data sources and the
  implementation of agents
  customer: 'DE: pharma'
  industry: pharma
  duration: 3m
  role: Tech consultant
  kpi: Submit the data platform architecture to enable proper data modeling and agentic workflow
  team: 3.0
  responsibilities: Conduct the visits with all business units, collect requirements
  and discuss feasible solutions, propose a 2y migration plan
  tech: snowflake, databricks
  label: data platform
  year: 2026
• id: 9l
  title: Cyber-security with GPUs
  desc: Clients struggle to parse security critical logs and they need GPUs to effectively run dedicated ML models to anticipate threads and spare batch processing. The added efficiency improves operations and reduces storage and batch processing. A model should be trained on their own workflow and specific data.
  customer: 'DE: automotive'
  kpi: train a model to run on nvidia GPUs for the specific customer need
  industry: automotive
  duration: 5m
  role: Tech consultant
  team: 5.0
  responsibilities: Collect the requirements, sync with nvidia about the implementation plan across 200 locations and 200k employees
  tech: nvidia morpheus
  label: cyber-security
  year: 2024
• id: 9e
  title: Agentic system
  desc: A complex manifacturing corporate need a strategy to design a data platform
  to enable agents to support their workforce. We consult the client in designing
  a platform that enable the integration and cosinstency of data sources and the
  implementation of agents
  customer: 'DE: pharma'
  industry: pharma
  duration: 3m
  role: Tech consultant
  kpi: Submit the data platform architecture to enable proper data modeling and agentic workflow
  team: 3.0
  responsibilities: Conduct the visits with all business units, collect requirements
  and discuss feasible solutions, propose a 2y migration plan
  tech: snowflake, databricks
  label: data platform
  year: 2026
• id: 9d
  title: IoT predictive maintenance
  desc: The client is too slow to fix some issues in their fleet and they need tools
  to predict the next issue to promptly plan maintenance. The platform should correctly
  collect and parse telemetry data and ML should reliably predict issues.
  customer: 'DE: automotive'
  kpi: present and implementation plan based on an accurate data analysis
  industry: automotive
  duration: 5m
  role: Tech consultant
  team: 5.0
  responsibilities: Conduct the client visit, talk with the relative stakeholders,
  run exploratory analytics and prepare the implementation plan
  tech: databricks
  label: predictive maintenance
  year: 2024
• id: 9c
  title: Insurance assistant bot
  desc: The insurance contract is pretty complex and the client need quick support
  to help their employees to respond to client's questions.
  customer: 'DE: insurance'
  industry: insurance
  duration: 3m
  role: Data scientist
  team: 1.0
  responsibilities: Devlop the tools to parse the contract information, tweak language
  specific issues and run tests and benchmarks to find the most suited model
  tech: 'llm, python '
  kpi: A functional MVP for their insurance consultancy bot
  label: support agent
  year: 2025
• id: 9b
  title: Highly qualified tech sales agent
  desc: 'The client has highly specialized scientific products which require highly
  qualify tech sales assistance which is hard to find. A well tuned RAG supports
  the client in finding a quick and reliable solution '
  customer: 'DE: bio'
  industry: bio
  duration: 6m
  role: Data scientist
  team: 5.0
  responsibilities: Parse all the documentation and develop the best suited tools
  for better accuracy. Review the infrastructure design and propose the selection
  of better suited tools.
  kpi: Overview the infrastructural issues preventing the agentic system to deliver the expected quality
  tech: llm, python
  label: sales agent
  year: 2024
• id: 9a
  title: Salesforce tech coach
  desc: Advising hardware resellers how to scope a business opportunity to propose
  their clients further investment in hardware facilities.
  customer: 'US: hardware'
  industry: hardware
  duration: 4m
  role: Tech consultant
  kpi: leads and opportunities
  team: 1.0
  responsibilities: Preparing the presentation material, travel in different countries,
  talking in different languages to the local salesforce.
  tech: '-'
  label: coaching
  year: 2024
• id: 8c
  title: 'Booklink: books into graphs 2023'
  desc: 'Project Description: Complex and long information can be challenging to understand
  and remember without mind maps that represent the most important concepts. Booklink
  summarizes the most relevant concepts and the connection between them displaying
  in an interactive graph. Language models summarize the content of the text and
  the connections between the nodes. '
  customer: 'EU: tech'
  industry: media
  duration: 9m
  role: 'Tech Lead '
  team: 2.0
  responsibilities: Deciding the tech stack productization of the code interacting
  with the language model Increase code quality for front-end, back-end and batch
  processes ML ops on aws and on prem
  tech: 'LLM, python, fastapi, D3.js, nodejs, aws, docker '
  kpi: deliver the MVP
  label: llm, summarization
  year: 2023
• id: 8b
  title: 'Analytics platform for sales emails '
  desc: 'Emailing in the 20s is still the most relevant mean for sales conversion
  but email delivery gets more and more challenging as providers improve their filters
  by recently adding language models. The company needed an analytics platform to
  understand the main KPIs of their business and operational efficiency. '
  customer: 'DE: tech '
  industry: sales
  duration: 6m
  role: Head of data
  team: 6.0
  responsibilities: "Collect requirements Propose the most suited data platform for fulfilling company's needs Implement the data platform on-prem with docker. Develop two custom middlewares Expose and document custom APIs Introduce tests and health checks"
  tech: 'Airflow, metabase, kafka, red-panda, traefik, nginx, postgres, python, go'
  kpi: deliver the complete data platform
  label: outreach, sales
  year: 2022
• id: 8a
  title: 'Language models for sales emails'
  desc: 'Sales emails need to be effective and concise, language models can help supporting
  sales force to make their messages more effective. '
  customer: 'DE: tech '
  industry: sales
  duration: 4m
  role: AI, software
  team: 3.0
  responsibilities: Ideate the solution and pick the tech stack Discuss technology
  moving components and change solutions as market tools change Fine tuning vs prompt
  eng vs embeddings Custom test models Write the prompts and the code Deploy the
  MVP
  tech: 'Nodejs, angular, LLM, custom middleware '
  kpi: implementation strategy for startups
  label: Outreach, sales, llm
  year: 2022
• id: 7c
  title: 'Agent compensation '
  desc: 'Project Description: Sales agents motivation is crucial to our client success
  and the best delivery for our AI solution. Compensation should be aligned to the
  most important client KPIs and the optimization metric. Which scenarios are the
  most effective for delivery the highest gain simulating from real life data? '
  customer: 'UK: telco '
  industry: telco, media
  duration: 1y
  role: AI lead
  team: 3.0
  responsibilities: Define with the customer the most important KPIs Collect the data
  and design the simulation Develop the models corresponding to each metric Simulate
  scenarios where each agent compete with other agents on each metric Assess and
  present the best strategy as return of investment
  tech: 'R, sql, stan'
  label: AI, game theory, telco
  kpi: Increase CLV by aligning company goals with agent compensation
  year: 2021
• id: 7d
  title: 'Mortgage installment collection uplift'
  desc: 'Project Description: Mortgage installments can be paused for different reason
  but the credit institute needs to restart collection incentivizing the debtor
  to find an agreement. An AI solution support the uplift in conversion for resuming
  the installment collection.'
  customer: 'IT: bank '
  industry: banking
  duration: 6m
  role: Data analytics, business case
  team: 6.0
  responsibilities: 'Dig into data sources, build KPIs and propose a metric Iterate
  with the AI teams to estimate an uplift on the metric provided by the ML model
  Iterate with the DE teams to build the pipeline to feed data into the AI model
  Test metric biases and propose the client operational changes to increase optimization
  capabilities'
  tech: 'Postgres, sql, python '
  kpi: Increase mortgage installment payment
  label: Banking, AI
  year: 2021

• id: 7c
  title: 'Metric and delivery performance supervision'
  desc: 'The company offers an AI solution for optimization. The metric has to be
  discussed with the client for providing the most effective business and operational
  value while being measurable, consistent and built on reliable data sources. The
  metric performances should be stable over time and its uplift forecasted to prevent
  mis expectation with the customer and internal stakeholders. '
  customer: 'UK: media '
  industry: media
  kpi: increase CLV revenue
  duration: 1y5
  role: 'Data analytics, developer'
  team: 40.0
  responsibilities: 'Sit with the business/operational client teams and understand
  their needs. Sit with the tech client teams and understand data sources and connectors.
  Propose a metric that is measurable, consistent and aligned to what our AI model
  can deliver. Customer lifetime value metric calculation.

  Predict an uplift and build the commercial proposal Instruct the DE, DA, DB, AI
  teams on how to work with that metric Test and monitor the processes, keep the
  client in the loop for reporting,'
  tech: 'Postgresql, stan, sklearn, R, gcp '
  label: media, metric, analytics, CLV
  kpi: Build reliable metrics and pipelines to reduce inconsistencies with sources and client reports
  year: 2020
  - id: 7b
  title: 'Architecture migration'
  desc: 'The client wants to move from their on-prem data infrastructure to a big
  cloud provider. '
  customer: 'IT: telco '
  industry: telco
  duration: 1y
  role: 'Data analytics, consiultant '
  team: 30.0
  responsibilities: Sit with the client teams and discuss all data sources and integrations
  in use. Propose which processes can be shifted on the cloud making sure efficiency,
  security and costs improve Work on every single pipeline checking data integrity
  and reporting.
  tech: 'Aws, gcp, Greenplum, IBM, oracle'
  kpi: submit an on-prem data platform
  label: telco, migtration, cloud
  year: 2020
  - id: 7a
  title: AI for voice outreach
  desc: 'The client wants to implement our company AI solution but it is not sure
  whether the technical implementation is feasible nor the revenue goals can be
  reached.'
  customer: 'DE: telco'
  industry: telco
  duration: 1y
  role: Tech sales
  team: 10.0
  responsibilities: Understand customer needs and tech stack Propose a technical solution
  and a commercial proposal. Consistency check on data quality and business estimates
  Review contract.
  tech: 'Sql, python '
  kpi: revenue forecasts
  label: telco, AI
  year: 2020
  - id: 6c
  title: 'Remote driving disconnection patterns'
  desc: 'Remote driving needs a reliable technology to enable drivers to log into
  connected cars and drive safely. Internet connection shows issues in providing
  reliable connection with cameras and car controls. A ML investigation need to
  find the root causes for disconnection'
  customer: 'DE : mobility '
  industry: transportation
  duration: 3m
  role: AI lead
  team: 5
  responsibilities: Collect all useful data and assess data quality Feature engineering
  and selection. Model selection and hyperparameter tuning Debugging of data inputs
  and re-phasing of information. Spot problem and feedback to engineering team.
  tech: 'Tensorflow, keras, ML, python, spark, kinesis'
  label: AI, telemetry
  kpi: reduce downtime
  year: 2020
  - id: 6b
  title: 'Blockchain in pharma '
  desc: 'Counterfeiting is a serious problem for pharma. Middleman warehouses in some
  countries swap packages and replace the original product with cheaper medicaments.
  Blockchain can ensure traceability and spot the transaction where counterfeit
  could have token place. '
  customer: 'MY: pharma '
  industry: pharma
  duration: 5m
  role: 'Teach lead '
  team: 2.0
  responsibilities: 'Decide the tech stack. Analyze existing data and estimate the
  phenomenon. Build app MVP Talk to investors and partners '
  tech: 'Aws, python, angular, blockchain '
  label: blockchain, pharma
  kpi: reduce counterfeits
  year: 2019
  - id: 6a
  title: 'Optimization engine for operations '
  desc: "Optimizing workforce in a vast operational area is crucial for company profitability. Workforce needs to drive from warehouses to each scooter in a city for operational tasks. A model is required to distribute workforce considering multiple constrains (working hours, travelled distance, tasks number\u2026) that accepts multiple status changes and keeps optimizing task distribution without disrupting current operations. "
  customer: 'DE : mobility '
  industry: mobility
  duration: 6m
  role: AI lead
  team: 6.0
  responsibilities: Go on field and collect all the task types and problems connected
  with operations Build graphs and routing software and put them in production Build
  a model to create and iterate routes and evaluate their efficiency Generate myriads
  of simulations and tune parameters. Deploy the model and test the API connection.
  Develop a middleware to convert the different data types Deploy the services on
  difference VPCs, configure load balancer, test Compare solutions with other software
  and present it
  tech: 'Aws, celery, redis, sklearn, tensorflow, python, fastapi'
  label: Logistic, reinforcement learning
  kpi: improve ops efficiency
  year: 2019
  - id: 5f
  title: 'Motorway stoppers '
  desc: 'The client needs to understand the potential of their user base comparing car drivers stopping at their stores compared to the ones stopping at competitor stores. Mobile network data is analyzed to understand motorway traffic and stops during users journey. '
  customer: 'DE : retail '
  industry: retail
  duration: 2y
  role: 'ML expert '
  team: 10.0
  responsibilities: Build filter to subset the user base to match customer requirements
  Iterate with the engineering team to improve software quality especially regarding
  routing Send the computation and transform the data. Analyze the data and compare
  with external sources. Build crawlers to retrieve updated information from internet
  to tune parameters. Use machine learning to further improve matching with external
  data and extrapolation factors. Build recurrent reports and keep the conversation
  with the client including future feature development
  tech: 'Sklearn, tensorflow, python, spark, yarn '
  kpi: increase conversion rate for retail
  label: mobility, telco
  year: 2018
  - id: 5e
  title: 'Motorway mean distances '
  desc: 'A big electric car manufacturer needs to know the mean travel distance in
  German motorways to plan the construction of electric charging stations for the
  given capacity of their cars. Mobile network data supports the estimation on how
  many users drive daily on the motorway network and sophisticated routing algorithms
  compute the exact junction points crossed. '
  customer: 'JP : automotive '
  industry: automotive
  duration: 2y
  role: 'ML expert, product owner '
  team: 10.0
  responsibilities: Iterate with the engineering team about the quality of the routing
  solution, testing and re-process data for each iteration. Develop spark functions
  to process the parquet files and extract the data needed. Build reports for the
  client and add consistency checks for the discussion. Data visualization and presentation
  at customer location
  tech: 'Hadoop, spark, yarn, python '
  label: Mobility
  kpi: planning of electrical charging stations
  year: 2018
  - id: 5d
  title: 'Retail and real estate visitors '
  desc: 'Retail stores need to understand the number of people passing in front of
  the building to establish the potential, the capture rate, the sales rate and
  building value. Additional information can be added to this user pool as origin,
  nationality, expense capability and socio-demo from their mobile data traces. '
  customer: 'EU : retail and real estate '
  industry: real estate
  duration: 6m
  role: Data scientist, product owner
  team: 4.0
  responsibilities: Define all the polygons covering the stores Define all the mobile
  cells covering the polygons. Develop a software to re-center user position based
  on cells information. Subset the user dwelling or passing by the polygon. Build
  reports and present them.
  tech: 'Hadoop, spark, yarn, python '
  kpi: Real estate properties value
  label: Retail
  year: 2018
  - id: 5c
  title: 'White spot analysis '
  desc: 'Retailer has a lot of data about sales compared to the geographical position
  of the store and they want to know what other locations can be suitable to open
  a new store with similar revenue benefits. '
  customer: 'US : restaurants '
  industry: retail
  duration: 1y
  role: Data scientist, product owner
  team: 5.0
  responsibilities: Analyze customer data, calcolate isochrones. Enrich customer data
  with dwellers report to calculate capture rate Analyze country wide data to spot
  locations with similar conditions outside of the isochrone areas covered by existing
  locations. Build report, data visualization and present to customer.
  tech: 'Tibco, qgis, python '
  label: Restaurant
  kpi: Planning for new retail locations
  year: 2018
  - id: 5b
  title: 'Mobile network analytical software '
  desc: 'A telco company operating in other countries established a partnership with
  us to enter their market with products similar to the ones we sell in Germany
  and they need consultancy to build the software to analyze their data. '
  customer: 'SE: telco '
  industry: telco
  duration: 2y
  role: 'ML expert, product owner '
  kpi: Data monetization
  team: 20.0
  responsibilities: 'Identify the most important stakeholders and establish the partnership.
  Prepare presentations and sessions for the knowledge exchange. Work on their reports
  and point out inconsistency and improvements. Support with their commercial proposals '
  tech: 'Hadoop, yarn, spark, python '
  label: mobility
  year: 2017
  - id: 5a
  title: 'Daily commuters '
  desc: 'A transportation company needs to know how many users use their services:
  where they start their journey, where they enter and leave their means of transport. '
  customer: 'DE: mobility '
  industry: mobility
  duration: 6m
  role: Data scientist, product owner
  team: 10.0
  kpi: Improve public transportation usage
  responsibilities: Iterate over different filters to subset the user base into daily
  commuters only Iterate over different algorithms to perform modal split Use the
  limited external data to tune own model parameters. Run the calculation and build
  the reports for the customer.
  tech: 'Hadoop, yarn, spark, python'
  label: mobility, telco
  year: 2017
  - id: 4e
  title: 'Advertisement platform syncing'
  desc: "In advertisement real time bidding is a technology that allows multiple players to trade creativities over internet. On top of seller and buyer platforms other platforms (data management platform, header bidder, ad server\u2026) need to be synced to participate to the bid and conclude the trade within 50ms. All of these platforms need to be configured and synced via scripts or tracking pixels."
  customer: 'IT: media '
  industry: media
  duration: 2y
  role: 'Data science, development '
  team: 20.0
  responsibilities: 'Speak with the vendor, test and configure different platforms:
  SSP, DSP, DMP, ad server, tag manager, optimization tools, header bidder. Speak
  with the service provider to prepare custom services for information syncing Dump
  the enriched data to the datalake'
  tech: 'Tealium, pubmatic, bluekai, krux, optimizely, nugg.ad '
  label: advertising
  kpi: Increase targeted spending
  year: 2016
  - id: 4d
  title: 'Organize corporate data platforms '
  desc: 'Multiple companies share the same revenue channel which is the sales house
  I represent. Data need to be shared into less platform to enable monetization.
  External platforms should dump data into a datalake to further enrich user profiling
  and useful targets for the industry. '
  customer: 'IT: media '
  industry: media
  duration: 2y
  role: 'Data science, development '
  team: 20.0
  kpi: Datalake foundation
  responsibilities: ' Talk with cloud providers (aws, gcp, azure) evaluate their proposal
  and test the solutions Define all the platforms that need to be integrated. Convince
  the stakeholders to participate to the program and support them in the migration.
  Scout technology providers that can support the coordination of data sources and
  pipeline ingestion '
  tech: 'Aws, gcp, azure, python '
  label: infrastructure
  year: 2016
  - id: 4c
  title: 'Data monetization: targeted audience '
  desc: 'Targeted advertising has much better conversion rates than any random advertisement.
  Good targets are hard to find and perform differently under different metrics.
  Data enrichment is a key component and first party data need to be enriched by
  external providers whose quality and reliability has to be proven. '
  customer: 'IT: media '
  industry: media
  duration: 2y
  kpi: Targeted audiences for advertising
  role: 'Data scientist'
  team: 12.0
  responsibilities: 'Define the valuable target for the industry, build a catalogue
  of audience Expand the targets across the user base using look-alike. Post evaluate
  the data especially for socio-demo. Assess target performances and use the data
  to improve target definition Enrich the data with third parties: retail and telco
  data. Scout the provider with the best linguistic model to label content and import
  the labels. Sync the data with relevant platforms. Monetize the data '
  tech: 'Tealium, pubmatic, bluekai, krux, optimizely, nugg.ad '
  label: media, advertising
  year: 2016
  - id: 4b
  title: 'Inventory forecast '
  desc: 'Sales force needs to sell two weeks in advance the available inventory. Accurate
  forecasts are essential to fulfill all available spots. '
  customer: 'IT: media '
  industry: media, advertising
  duration: 2y
  role: 'Data scientist '
  team: 3.0
  kpi: Improve selling inventory
  responsibilities: 'Define all the different inventory types. Implement different
  forecast methods and tune them. Run a loop on historical data and watch the competition
  among models per inventory type. Select the most performant model per inventory
  type. Run the forecast each week and communicate with the marketing team the available
  inventory for the next two weeks. Analyze the sold and unsold and iterate for
  the subsequent week '
  tech: 'R, python '
  label: media
  year: 2016
  - id: 4a
  title: 'Data visualization and BI '
  desc: 'Data insights need to be access quickly and many visualization types should
  be used to represent the most important feature of each analysis. Over years the
  environment of available tools changed a lot and solutions adapted to current
  trends. '
  customer: 'IT: media'
  industry: media, e-commerce
  kpi: data analytics tools for finance
  duration: 5y
  role: 'Data analytics, developer '
  team: 20.0
  responsibilities: 'Understand which data should be used by which stakeholder. Agree
  on the time aggregation and graph type. Build views and aggregated tables in the
  database Schedule the jobs for aggregated tables. Find the most suited BI Grant
  permissions or connect with other authentication tools '
  tech: 'sql, postgres, mongo, neo4j, elastic '
  label: BI
  year: 2015
  - id: 3c
  title: 'E-commerce booking uplift: personalized user interface '
  desc: "The booking funnel is leaky because some users don't enjoy/understand
  the user interface and leave for looking at a competitor solution. UI/UX and
  personalization tests help the developer team decide for the best solution to
  put in production. A/B test is a reliable way of understanding which solution
  is increasing the overall sales of the e-commerce portal. "
  customer: 'DE: airline '
  industry: transportation
  duration: 1y5
  role: data science, product owner
  kpi: 7 digits more booking revenue
  team: 25.0
  responsibilities: 'Identify the weak spots with the help of analytics tools. Design
  and propose a test to put in production. Convince the stakeholders presenting
  the feasibility study and revenue estimate. Talk to agencies and instruct them
  how to implement the test. Put the test live and monitor performances. Analyze
  the different variants and present the results. Calculate the revenue uplift and
  get confirmation from the controlling team about the correct attribution to the
  team. Iterate over 30 times in a year '
  tech: 'Tealeaf, webtrack, maxymizer, R '
  label: e-commerce, uplift
  year: 2014
  - id: 3b
  title: 'Customer feedback and sentiment analysis on e-commerce '
  desc: 'An important tool to understand the goodness of your portal is the direct
  voice of users and customers. The website has a chatbot, a feedback tool and interaction
  logging and customers write to company CRM. Users write on social and affect brand
  reputation. All of this text need to be parsed and analyzed to understand how
  successful the brand and the product are. '
  customer: 'DE: airline '
  industry: transportation
  duration: 1y5
  role: data science, product owner
  kpi: improve customer satisfaction
  team: 25.0
  responsibilities: 'Collect the onsite data sources Collect the providers data sources.
  Crowl information from internet. Parse the data (stem, stopwords, clean). Build
  own specific dictionary. Run sentiment analysis. Add external tracking information.
  Gain insight from outstanding comments. Write report and let it circulate '
  tech: 'Tealeaf, webtrack, NLP, R, voice of customers '
  label: customer care
  year: 2014
  - id: 3a
  title: Customer personalization program cross corporate
  desc: 'Different companies within the corporate group need to define a common strategy
  to organize customer information and build personalized offers and experiences.
  The information is spread across multiple silos and contact channels. The group
  needs to build a datalake and a strategy to contact all users through the different
  contact channels (e-commerce, email, call center, advertising, premium program)
  with a consistent communication and ad-hoc offering. '
  customer: 'DE: airline '
  industry: trasportation
  duration: 1y5
  kpi: unify corporate data
  role: data science, product owner
  team: 25.0
  responsibilities: Define requirements for the datalake and analytical platform.
  Scout the different data sources and speak with integration providers on how to
  ingest the data Design the products that will access user information and how
  those tools will delivery content. Design the type of personalization for each
  communication channel Iterate with many consultancy agencies for every specific
  project Present and report project results to multiple stakeholders. Write report
  and let it circulate
  tech: 'Tealeaf, webtrack, NLP, R, voice of customers
  '
  label: e-commerce
  year: 2014
  - id: 2b
  title: 'ERP in the cloud '
  desc: 'Different SMEs need to move from on-site IT to cloud solutions but they need
  an ERP flexible enough to fulfill their specific business needs. '
  customer: 'IT & TZ: SME '
  industry: SME
  duration: 1y5
  role: 'software engineer, consultant'
  kpi: custom ERP modules for SME
  team: 5.0
  responsibilities: 'Talk to client and understand their needs. Scout the most suited
  solution for their needs. Develop modules for specific business needs. Configure
  the software. Build ad-hoc middlewares '
  tech: 'python, java, sql, php, android '
  label: ERP, cloud
  year: 2013
  - id: 1b
  title: 'Fusion pathway for lipid membranes '
  desc: 'Cells are made of lipid membrane that isolate the interior of the cell from
  the exterior but show many different behaviors that allow the cell to perform
  certain types of exchanges with the external environment. One of the most important
  is fusion where the roles of lipids and peptides is not well modeled yet. '
  customer: 'DE: University/biophysics '
  industry: research
  kpi: provide the pharma industry with reliable insights about transmembrane stability
  duration: 3y5
  role: 'Research assistant, software developer '
  team: 15.0
  responsibilities: 'Model the components of the simulation Write the code and define
  each iteration step Parallelize the code with map reduce Tune the code Run the
  code in the cluster and analyze the data Develop the data visualization suite
  for 3D data exploration '
  tech: 'C, c++, Qt, OpenGL, openMP, MPI '
  label: research, biophysics
  year: 2008
  - id: 1a
  title: 'Collaborative research center '
  desc: 'Part of a graduate school and a national research project to contribute to
  specific topics in biophysics and neuroscience. National and external investors
  were requesting progress reports and talks to track progresses in the funded research
  topics. '
  customer: 'DE: university'
  kpi: deliver a cross collaborative research project
  industry: education
  duration: 3y5
  role: 'Research assistant '
  team: 30.0
  responsibilities: 'Teach and tutor students. Present to conferences. Present to
  internal progress reports. Prepare application for funds Publish articles '
  tech: 'c, c++ '
  label: education
  year: 2008

• type: work
  year: 02-23/present
  role: Lead AI/generative solutions for cloud and on-prem
  company: Softserve
  description: 'Tech presales and service professional positioned between hardware
  producers (nvidia, lenovo, dell, cisco...) and the market to consult, teach and
  realize ML/AI projects with the use of the most modern solutions both on the appropriate
  hardware (cloud/on-prem) or software.'
• type: work
  year: 09-22/01-23
  role: Strategic data consultant/LLM
  company: startups + lightmeter@Y-combinator
  description: 'Consulting early stage start-ups to architect and deliver data infrastructure: storage, interfaces, middlewares, scheduler, messaging, security, redundancy, analytics, BI. Train and implement language models with focus on email outreach.'
• type: work
  year: 09-20/07-22
  role: Director client data services/telco
  company: afiniti
  description: 'Leading the technical implementation of afinti AI solutions for big
  European clients (telco, banking, media) and managing remote teams assigned to
  the project: DE, DA, DS, AI, DB admin. Responsible in customer projects: pre-sales,
  feasibility study, metric design, data reliability, data analytics, performance
  monitoring, billing, financial reports.'
• type: work
  year: 09-19/09-20
  role: AI lead/mobility
  company: circ/bird + vay
  description: 'Developing a full stack (micro service based) solution for fleet dispatch.
  The engine defines the most profitable task assignments for the capable agents
  on the field. Demand forecast and customer patterns. Forecast on telemetry data.'
• type: work
  year: 10-17/09-19
  role: Sr DS/mobility
  company: Motionlogic@Deutsche Telekom
  description: 'Interpreting mobile data of 30M users and their mean of transportation,
  product owner for product development of origin-destination matrices, responsible
  for customer deliveries (mobility, advertising, real estate, commuter patterns).
  Machine learning, insight highlights and data monetization.'
• type: work
  year: 10-16/10-17
  role: Sr DS/advertising
  company: Mediamond@Mediaset
  description: 'Data driven advertising via audience segmentation on a large cross
  device media network. Responsible for target quality and performance, inventory
  forecast, modeling, data visualization, and business intelligence. Tech consultant
  for user profiling built from web logs, CRM data, second party enrichment, semantic
  engines, and SEO keywording. Representing my company for the big data project
  of the corporation.'
• type: work
  year: 10-14/01-16
  role: Data scientist/e-commerce
  company: E-dynamics@Lufthansa
  description: 'Data driven segmentation of price and ancillary teasers on lufthansa.com.
  Campaign design, tracking, revenue calculation, reporting and tool concept development.
  Owner of 30+ A/B tests with 3M certified revenue uplift.'
• type: work
  year: 02-13/10-14
  role: Data consultant/ERP & BI
  company: dauvi
  description: 'Advising SMEs to organize and collect data in their companies, installing
  cloud based ERP/CRM systems. Setting a geo based rural portal to connect professionals.'
• type: work
  year: 06-09/01-13
  role: Research assistant/biophysics
  company: "UniGö"
  description: 'Developing a parallel high performance c++ suite for Monte Carlo and
  Molecular Dynamics simulations, openGL 3D visualization, ETL, data visualization
  and reporting for biological oriented questions. Publications: Influenza Fusion,
  String Method, Pore Formation.'
• type: education
  year: 06-09/01-13
  role: PhD
  company: "UniGö"
  description: Study of protein-induced pore stability via profiled parallel computing
  on multiprocessor clusters via MonteCarlo and molecular dynamics methods and PDE.
  Visualization of statistical properties of data and study on membrane morphology
  transformations.
• type: education
  year: 01-07/03-09
  role: Master
  company: UniPd
  description: Nanoparticle stability in lipid membranes via molecular dynamics simulations
  and visualization tools. Parallel code with revision control.
• type: education
  year: 10-02/12-06
  role: Bachelor
  company: UniPd
  description: Monte Carlo simulations of ion-defects' diffusion after distribution
  calculation of sputtering of silicon ions on a silicon lattice.
• type: training
  year: Q3/12
  role: trainee
  company: UniHB
  description: Tuning of parallel programs
• type: training
  year: Q3/13
  role: trainee
  company: "UniGö"
  description: Management consulting & project management
• type: training
  year: Q1/14
  role: trainee
  company: odoo.com
  description: ERP setting up and programming
• type: training
  year: Q2/14
  role: trainee
  company: ander35
  description: 'Entrepreneurship: market, strategy and business plan'
• type: training
  year: Q1/17
  role: trainee
  company: hfarm
  description: 'HackItaly: 48h app dev'
• type: training
  year: Q2/20
  role: trainee
  company: xpeppers
  description: Aws training courses
• type: training
  year: Q2/20
  role: trainee
  company: entrepreneur first
  description: 'Entrepreneurship program: blockchain in pharma'
• type: awards
  year: Q3/13
  role: creator
  company: Regione Lombardia
  description: Fund for supporting cultural and creative ideas
• type: awards
  year: Q1/13
  role: creator
  company: "Comunità Montana ValSabbia"
  description: "Ideas' competition Valsabbia e Garda"
• type: awards
  year: Q3/15
  role: creator
  company: Maker faire Rome
  description: Maker of merit

• topic: languages
  skill: 'Native: Italian. Fluent: English, German, Spanish Intermediate: French,
  Portuguese'
• topic: programming
  skill: python, js, c++, c, R, spark, go (viz) openGL, Qt, GTK+
• topic: machine learning
  skill: tensorflow, keras, sklearn, scipy, caret, pytorch
• topic: AI
  skill: gan, reinforcement learning, forecasts, predictions, classifications
• topic: web
  skill: html5, css3, js, d3, react, angular
• topic: server
  skill: "fastapi, celery, redis, nginx, traefik, nodejs, php"
• topic: databases
  skill: ' SQL, postgresql, cassandra, mongodb, neo4j, elastic (fs) hdfs, s3'
• topic: development
  skill: ' git, gitlab, svn, CI/CD, pytest, docker'
• topic: production
  skill: ' docker swarm, kubernetes, airflow, kafka, presto, terraform'
• topic: visualization
  skill: ' kibana, grafana, metabase, powerBI, tableau'
• topic: IoT
  skill: ' mqtt, mosquitto, arduino, esp32, teensy, seeed, reaspberry pi, jetson'
• topic: perception
  skill: ' computer vision, 3d modeling, reinforcement learning, embedded programming'
• topic: numeric
  skill: ' numpy, gsl, CGAL, matlab, octave, maple, root (grid) MPI, openMP'
• topic: software
  skill: ' (GIS) qgis (viz) mayavi, povray (CAD/3D) rhino, blender, cura'
• topic: LLM
  skill: ' RAG, graphRAG, lanchain, langsmith, llamaindex'
• topic: aws
  skill: ' s3, iam, ec2, sagemaker, bedrock, ecs, cloudfront, lambda, athena'
• topic: gcp
  skill: ' bigquery, kubernetes, pubsub, gemini'
• topic: azure
  skill: ' factory, kusto, ml studio, synapse, databricks, cosmos'
• topic: nvidia
  skill: ' cuda, ominverse, morpheus, nemo, nim, tensorrt, triton'
• topic: cisco
  skill: ' UCS, meraki, splunk'