Peter Verheijen

Managed two engineering tracks: a central ML/Data platform team and 4 embedded MLOps engineers across product teams. Established a dedicated MLOps function and modernized product serving infrastructure, enabling multi-version support and reducing tech debt.

Managed the MLOps team (4 engineers) by having 121's, discussing and assisting them with their personal growth plans and providing feedback for the growth reviews. Improved inter-and intrateam collaboration and effectiveness by organising alignment rituals (shared design sessions), team events, and project boards for better clarity and delivery.

Worked on deploying machine learning models in productions following MLOps principles (>10), by furthering the development of our Model Training Framework (built with MLflow) and refactoring existing models and specifying the roadmap in terms of work to be done for productionizing. Lowered the time to (re)deploy models into production (by several months), by standardizing the development of machin

Designed and Developed a Data & Machine Learning Platform, in collaboration with Databricks (Partnership), by managing the platform (functional and infrastructure) and onboarding teams, data pipelines and models. Lowered the operational costs of several production models (from >100k to <5k per year), by migrating these models from Azure Durable Functions to Databricks (Pandas UDFs).

Raised funds for the development of our SaaS company, by raising Angel and Seed funding (>200k), by conducting problems & solution interviews, writing an investment proposal and pitching our idea. Developed a SaaS offering for Fashion Retail Analytics, by doing two separate PoCs and developing an MVP, that involved loading data to our cloud DWH (AWS Redshift) and providing insights through our cus

Provided the management team with updates on our Machine Learning efforts, by giving weekly presentations, where I presented the sales forecasts and the performance of the basket recommendation model. Improved our inventory management, by reducing the out-of-stocks (by 30%), through sales forecasting by analyzing consumer baskets in our Data Warehouse (BigQuery).

Worked as a consultant for large Dutch clients such as Jumbo, Friesland Campina and Royal Ahrend, by completing client projects that involved developing an API (Django REST Framework) and a graph-based (NEO4j & Cypher) Recommender System.

Learned how to develop APIs and Full Stack Web applications, by passing weekly assignments and creating a Full Stack application, whilst following a Full Stack Web Development Bootcamp.

Grade: 7.5

Activities and societies: Specialized in Business Analytics and Quantitative Marketing.

Studied Econometrics and Management Science, specializing in Business Analytics and Quantitative Marketing. Gained expertise in statistical modeling, data analysis, and decision-making.

Grade: 7.0

Activities and societies: Focused on mathematical methods and statistical analysis.

Completed a Bachelor of Science in Econometrics and Operations Research. Developed a strong foundation in mathematical methods, statistical analysis, and optimization techniques.