spiega business
working experience for business use cases
1. spiega business
\(a+b\)
Here I collect all the experiences I accumulated over 17y or career with a focus on business use cases.
2. profile
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. 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. 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.
3. functions
I covered a diverse range of roles and skills across different industries and technologies. Here's a summary of the key roles:
- Tech Consultant:
- Client visits and site assessments for various technologies (e.g., AI solutions, IoT systems).
- Project management from inception to completion.
- Data Science:
- Conducting exploratory data analysis and modeling.
- Developing ML models for predictive maintenance, optimization, and automation.
- Analyzing customer data for targeted marketing strategies.
- Tech Consultant/Technical Lead:
- Designing and implementing technical solutions (e.g., AI platforms, IoT systems).
- Collaborating with development teams to integrate new technologies into existing products or services.
- AI Lead:
- Developing and deploying AI models for various applications.
- Conducting simulations and optimizations using machine learning algorithms.
- Project Manager/SME/Lead:
- Leading multi-disciplinary projects across different industries (e.g., automotive, energy, pharma).
- Coordinating resources and ensuring project timelines and goals are met.
- Data Architect/Data Engineer:
- Designing data infrastructure to support various data products.
- Integrating technologies like databases, cloud platforms, and AI systems.
- Sales/Marketing/Consultant:
- Developing sales strategies for customer acquisition.
- Collaborating with customers to solve complex technical problems and implement solutions.
- Technical Consultant/Collaborator:
- Providing technical guidance and support for various projects.
- Ensuring alignment between business goals and technological solutions.
- Data Analyst/Data Engineer/Consultant:
- Analyzing data and providing insights for decision-making.
- Integrating new technologies into existing systems to improve operational efficiency.
- Customer Support Specialist/Agent:
- Providing support for customers through various platforms (e.g., chatbots, email).
- Ensuring customer satisfaction and timely resolution of issues.
This diverse range of roles demonstrates your comprehensive expertise in various technical fields and areas related to data-driven solutions, project management, technology implementation, AI development, and business strategy.
4. verticals
Here is a list of verticals where I participated to different projects: These industries cover various technological areas such as AI, ML, data science, engineering, software development, operations management, and more.
4.1. automotive
- Energy Management (id: 1)
- Conduct client visit, factory and data room visit.
- Talk with all stakeholders and design an action plan for energy efficiency.
- First Level Support Bot (id: 2)
- Conduct client visit.
- Design the implementation plan.
- Create mockup for the client to test.
IoT Predictive Maintenance (id: 5)
- Conduct client visit.
- Talk with relative stakeholders.
- Run exploratory analytics.
- Prepare the implementation plan.
- Motorway Mean Distances (id: 21)
- Iterate with engineering team about the quality of routing solution, testing and re-process data for each iteration.
- Develop Spark functions to process parquet files and extract needed data.
- Build reports for client and add consistency checks.
- Data visualization and presentation at customer location.
4.2. energy
- Pipeline Operations (id: 3)
- Collect all data for Bavarian gas subnet.
- Create graph and map quantities to respective nodes.
- Run simulations, benchmark ML results.
4.3. pharma and lifescience
- Agentic System (id: 4)
- Conduct client visit.
- Collect requirements and discuss feasible solutions.
- Propose a 2-year migration plan.
Highly Qualified Tech Sales Agent (id: 7)
- Parse all documentation.
- Develop best-suited tools for better accuracy.
- Review infrastructure design and propose selection of better suited tools.
- Blockchain in Pharma (id: 18)
- Decide tech stack.
- Analyze existing data.
- Estimate phenomenon.
- Build app MVP.
- Talk to investors and partners.
4.4. FSI
Insurance Assistant Bot (id: 6)
- Develop tools to parse contract information.
- Tweak language-specific issues.
- Run tests and benchmarks to find most suited model.
- Mortgage Installment Collection Uplift (id: 13)
- Dig into data sources.
- Build KPIs and propose a metric.
- Iterate with AI teams to estimate uplift.
- Iterate with DE teams to build pipeline.
- Test metric biases.
- Propose client operational changes.
4.5. manifacturing
- Salesforce Tech Coach (id: 8)
- Prepare presentation material.
- Travel in different countries, talk in different languages to local salesforce.
4.6. tech industry
- Booklink: Books into Graphs 2023 (id: 9)
- Decide tech stack productization of code interacting with language model.
- Increase code quality for front-end, back-end and batch processes.
- ML Ops on AWS and on-prem.
Analytics Platform for Sales Emails (id: 10)
- Collect requirements.
- Propose 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.
- Language Models for Sales Emails (id: 11)
- Ideate the solution.
- Pick tech stack.
- Discuss technology moving components and change solutions as market tools change.
- Fine-tuning vs prompt engineering vs embeddings.
- Custom test models.
- Write prompts and code.
- Deploy MVP.
4.7. telco and media
- Agent Compensation (id: 12)
- Define with customer the most important KPIs.
- Collect data and design simulation.
- Develop models corresponding to each metric.
- Simulate scenarios where each agent competes on each metric.
- Assess and present best strategy as return of investment.
- Metric and Delivery Performance Supervision (id: 14)
- Sit with business/operational client teams.
- Sit with tech client teams.
- Propose a metric that is measurable, consistent and aligned to AI model.
- Calculate customer lifetime value (CLV).
- Predict uplift and build commercial proposal.
- Instruct DE, DA, DB, AI teams on working with metric.
- Test and monitor processes.
- Keep client loop for reporting.
- Architecture Migration (id: 15)
- Sit with client teams.
- Discuss data sources and integrations in use.
- Propose processes to shift on cloud improving efficiency, security, costs.
- Work every single pipeline checking data integrity and reporting.
- AI for Voice Outreach (id: 16)
- Understand customer needs and tech stack.
- Propose technical solution and commercial proposal.
- Consistency check on data quality and business estimates.
- Review contract.
- Mobile Network Analytical Software (id: 24)
- Identify most important stakeholders and establish partnership.
- Prepare presentations and sessions for knowledge exchange.
- Work reports, identify inconsistencies and improvements.
- Support with commercial proposals.
4.8. mobility
- Remote Driving Disconnection Patterns (id: 17)
- Collect useful data.
- 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.
- Optimization Engine for Operations (id: 19)
- Go on field.
- Collect task types and problems connected with operations.
- Build graphs and routing software in production.
- Create model to create and iterate routes.
- Evaluate efficiency.
- Generate myriads of simulations and tune parameters.
- Deploy model and test API connection.
- Develop middleware to convert data types.
- Deploy services on different VPCs, configure load balancer.
- Test.
- Compare solutions with other software.
- Present.
- Daily Commuters (id: 25)
- Iterate over different filters to subset user base into daily commuters only.
- Use algorithms to perform modal split.
- Run model with limited external data to tune parameters.
- Run calculation and build reports for customer.
- Organize Corporate Data Platforms (id: 27)
- Talk with cloud providers, evaluate proposals.
- Define platforms that need integration.
- Convince stakeholders to participate in program and support migration.
- Scout technology providers for data source coordination and pipeline ingestion.
- Data Monetization: Targeted Audience (id: 28)
- Define valuable target for industry, build catalogue of audience.
- Expand targets across user base using look-alike.
- Post evaluate data especially for socio-demo.
- Assess performance and use data to improve target definition.
- Enrich data with third parties: retail and telco data.
- Scout linguistic model provider for labeling content.
- Sync data with relevant platforms.
- Monetize data.
- Inventory Forecast (id: 29)
- Define different inventory types.
- Implement forecast methods and tune them.
- Loop on historical data, watch competition among models per inventory type.
- Select most performant model per inventory type.
- Run forecast each week and communicate with marketing team available inventory.
- Analyze sold and unsold to iterate subsequent weeks.
- Data Visualization and BI (id: 30)
- Understand data should be used by which stakeholder.
- Agree on time aggregation and graph type.
- Build views, aggregated tables in database.
- Schedule jobs for aggregated tables.
- Find suited BI tools.
- Grant permissions or connect with authentication tools.
4.9. retail
- Motorway Stoppers (id: 20)
- Build filter to subset user base matching customer requirements.
- Iterate with engineering team to improve software quality especially regarding routing.
- Send computation and transform data.
- Analyze data and compare with external sources.
- Build crawlers to retrieve updated information from internet.
- Use machine learning to further improve matching with external data.
- Build recurrent reports.
- Keep conversation with client including future feature development.
- Retail and Real Estate Visitors (id: 22)
- Define polygons covering stores.
- Develop software to re-center user position based on cells information.
- Subset users dwelling or passing by polygon.
- Build reports and present them.
- White Spot Analysis (id: 23)
- Analyze customer data, calculate isochrones.
- Enrich customer data with dwellers report to calculate capture rate.
- Analyze country-wide data to spot locations with similar conditions outside of existing locations.
- Build report, data visualization and present to client.
- Advertisement Platform Syncing (id: 26)
- Speak with vendors, configure platforms: SSP, DSP, DMP, ad server, tag manager, optimization tools, header bidder.
- Test and configure different platforms.
- Dump enriched data to datalake.
4.10. airline
- E-commerce Booking Uplift: Personalized User Interface (id: 31)
- Identify weak spots using analytics tools.
- Design and propose a test for production.
- Convince stakeholders about feasibility study and revenue estimate.
- Instruct agencies to implement test.
- Put live and monitor performances.
- Analyze different variants, present results.
- Calculate uplift and get confirmation attribution team.
- Iterate over 30 times per year.
- Customer Feedback and Sentiment Analysis on E-commerce (id: 32)
- Collect onsite data sources, providers' data sources, crawl internet.
- Parse data, stem, stopwords, clean.
- Build specific dictionary, run sentiment analysis.
- Add external tracking information.
- Gain insight from outstanding comments.
- Write report and circulate.
- Customer Personalization Program Cross Corporate (id: 33)
- Define requirements for datalake and analytical platform.
- Scout data sources, integrate with integration providers.
- Design products accessing user info delivery content.
- Design personalization for each communication channel.
- Consult agencies for projects and present reports to stakeholders.
4.11. consulting
- ERP in the Cloud (id: 34)
- Talk to client, understand needs.
- Scout suited solution.
- Develop modules business needs.
- Configure software.
- Build ad-hoc middlewares.
4.12. research
- Fusion Pathway for Lipid Membranes (id: 36)
- Model components simulation, write code each iteration step.
- Parallelize code map reduce, tune code.
- Run in cluster analyze data.
- Develop data visualization suite 3D data exploration.
- Collaborative Research Center (id: 37)
- Teach and tutor students.
- Present to conferences, progress reports.
- Prepare application for funds.
- Publish articles.
5. customer contact points
I have been working in companies with a focus on customer contact points:
- developing AI products to improve customer retention, upsell and seller performances.
- improving in-site performances by running 30+ optimization tests with 7digits return in a year.
- emails are still the most important tool for direct B2B sales. Improving metrics like open rate and whitelisting mean a big return
- targeted advertising means efficient coordination between platforms and intelligent parsing of scarce user information. Any improvement of metrics: ctr, view thru, cpc… makes a big difference in spending