Robin Opdam
Data Scientist at Metyis
Currently also working on:
- Learning about and working on generative AI applications.
- Architectural (cloud) Design
About me
I am a results-driven Data Scientist with a strong focus on delivering impactful solutions
that bridge technical innovation with real-world business challenges. With expertise in ML
Engineering, Data Engineering, Generative AI, and MLOps, I have successfully led machine learning
models from ideation to production, consistently driving measurable outcomes.
My ability to translate complex technical concepts into actionable insights allows me to build strong relationships
with stakeholders at all levels. I pride myself on fostering collaboration
between technical and non-technical teams, ensuring that every project aligns with strategic goals and delivers tangible value.
Explore my portfolio to see how I combine technical excellence, communication, and a passion for
impact across my projects, professional experiences, and educational journey.
Tech Stack/Experience:
- Data Science: Python PySpark, SQL, NoSQL, R
- Machine Learning: Scikit-learn, Spark, MLlib, Tensorflow, MLFlow
- Data Engineering: Azure Data Factory, ELT/ETL pipelines, blob storage
- Product Management: Azure Boards, Jira, Confluence, Azure Wiki
- Experimentation Design: Hypothesis Testing (A/B frameworks)
- DevOps (CI/CD): Azure, Gitlab, Github, DevOps, GCP
- Database: BigQuery, MySQL, NoSQL (CosmosDB)
- Visualisation: PowerBI, Looker Studio, Matplotlib, Plotly Dash, Shiny
- App Deployment: Docker, GCP, Azure, Heroku (FastAPI, OAUTH)
- IDE/Platforms: VSCode, Databricks, Jupyter, RStudio
Projects & Activities
Start-ups and Pancakes
Ongoing
Start-ups and Pancakes
Founded and led a community connecting impactful entrepreneurs, 5 events and counting! With 25+ members, including 5 founders.
More sessions to come! Feel free to reach out if interested.
Ongoing
Contributions to Chainlit
Chainlit is an open-source async Python framework that makes it incredibly fast to build Chat GPT like applications with your own business logic and data.
More contributions to come!
Dec, 2023
Ergobot, the ergogenic chatbot
Definition: "An ergogenic aid is any substance or phenomenon that enhances performance".
Ergo-log collects studies on ergogenic aids, the Ergobot is a smart chatbot interface based on ChatGPT and a vector database of scraped Ergo-log articles.
The people at Ergo-log enjoy sporadically using Ergobot!
Aug, 2023
Deploy Containerized Plotly Dash App with CI/CD (P2: GCP)
Jan, 2023
A sequel to deploying a containerized Plotly Dash app (to GCP)
With Heroku moving to paid tiers I wanted to try Google Cloud Platform for deploying the same container. This is a sequel to deploy the same container from part 1 using a CI/CD pipeline to deploy a container to Google Cloud Run.
Jan, 2023
Student placement tool for yearly 'bedrijvendag' of JSVU
Every year the Juridische Studenten Vereniging te Utrecht (JSVU) organizes a 'bedrijvendag' where students and companies get introduced to eachother based on student preferences and study year.
Created an online tool where the students, their preferences and the available companies and introduction rounds can be uploaded. The tool instantly produces a student matching that can be downloaded as an excel file.
Used OOP in Python, Docker, Github Actions, Dash, and Heroku to have the online tool always available and deploy any future changes through CI/CD
Jan-Mar, 2022
Deploy Containerized Plotly Dash App with CI/CD (P1: Heroku)
June, 2021
A guide on deploying a containerized Plotly Dash app
Step-by-step guide on how to use Github Actions to build a CI/CD pipeline and deploy a containerized Plotly Dash app to Heroku.
- Towards Data Science article
- App is now deployed on GCP (see P2 above)
- Code Repository
June, 2021
Focus
March, 2021
An article on how to tap into your focus and keep it
A brief summary of things I picked up on how to focus, hope it enables others to focus as well!
March, 2021
Master Thesis: Comparison of Deep Learning Product Recommendation Engines in Different Settings
This work sheds a different light on the differences between deep learning and classical recommendation algorithms by comparing them on both a Movie dataset and a Fashion dataset. Their performance is thoroughly compared based on their implementation and the resulting Recall@n and NDCG@n scores.
Feb-Aug, 2020
Portfolio Website
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You are here
Ongoing
Created this portfolio website using HTML, CSS and JavaScript
Launched in November 2020
Ongoing
Lead Data Scientist
From Jul 2024
Metyis: Lead Data Scientist
Leading teams and architectural design for Analytical, ML and GenAI use cases with our clients and within Metyis.
From Jul 2024
Analyst Data Scientist
Nov 2020-Jun 2022
Metyis: Analyst Data Scientist
Working with different clients, technologies and teams to bring impact.
Next to data science projects I worked on data engineering, ML engineering and data visualization
Nov 2020-Jun 2022
Associate Data Scientist
From Jul 2022
Metyis: Associate Data Scientist
Responsible for Data and AI projects from ideation to implementation, driving impact with our clients.
Led the company-wide Data Science Best Practices L&D program.
From Jul 2022
Intern Data Analyst
Aug, 2018
Schiphol: Intern Data Analyst
Analysed passenger security data to provide insights in staffing for different security filters. Introduced a new measure which combined several known features to measure under- and overstaffing of the security lanes.
Used Python and R for Exploratory Data Analysis
Created an R Shiny tool to view the trend of this new measurement in parallel with for example, throughput time.
Aug, 2018
Graduate Intern Data Scientist
Feb-Aug, 2020
Metyis: Graduate Intern Data Science
Applied business rules to improve recommendations on the website of one of its clients using Python
Built a dashboard combining different marketing channels with Funnel.io for campaign tracking using Google Data Studio
Wrote my MSc. thesis about a Comparison of Deep Learning Product Recommendation Engines in Different Settings (9/10)
Feb-Aug, 2020
MSc. Business Analytics
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Magna Cum Laude
Courses include: Advanced Machine Learning, Reinforcement Learning, Data Mining Techniques, Information Retrieval and Statistical Models.
2018-2020
VU: BSc. Business Analytics
The objective of this two year master programme is to enrich our understanding of the applications of Data Science and Optimisation to in-company problems. The programme offers a broad selection of courses in which I focused on machine learning
With great interest in the subjects I finished the programme with an average of 8.5/10 and a 9/10 for my thesis.
2018-2020
Exchange Programme
Courses include: Operations Management, Quantatative Risk Analysis, Strategy
2017
SMU: Exchange Programme
Living and studying abroad have brought me many things including: international relationships, different cultural views, proficiency in the english language.
Important is that it has showed me the possibilities of one's future are endless if you are willing to put in the work.
2017
BSc. Business Analytics
Courses include: Advanced Programming, Stochastic Modeling, Statistical Data Analysis, Machine Learning.
2015-2018
VU: BSc. Business Analytics
A combination of Computer Science, Mathemathics and Business management focussed on the transformation and handling of big data. The programme is based on practical assignments that provide hands on experience with dissecting problems, analysing and optimisation.
Finalised the programme with a project analysing bottlenecks within Schiphol Security lanes data using Python and R, Grade: 8/10
2015-2018
Happy to connect!
robinopdam@hotmail.com