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Upgraded Marketing Mix Modeling
A deep dive into modern MMM techniques, moving beyond simple regression into Bayesian approaches.
Senior Data Scientist · PhD Mathematician · Educator
Dr. Robert
Kübler
I take ML problems from proof to production, mostly in forecasting, causal inference, and Bayesian methods, and I put real effort into making the people around me better at the same. PhD in mathematics. 7+ years across research, engineering, and leadership.
- Revenue impact
- €10M+
- Article reads
- 1M+
- Professionals taught
- 50+
About
From pure math to production ML
Hi, I'm Robert. I'm a Senior Staff Data Scientist at ALDI DX, where I set the technical direction for a supply chain forecasting product optimizing warehouse staff planning across 150+ time series. I also mentor data scientists, design and deliver official ML curricula, and write for a 1M+ readership. My PhD in mathematics gives me the depth to go from a whiteboard proof to a production model without losing either rigour or pragmatism.
I care deeply about sharing what I know and making the people around me better.
90+ articles on Medium, the official sktime forecasting curriculum for the German Center for Open Source AI, talks at data science meetups (Zalando, Inovex), and shipped features to open-source libraries including scikit-lego and PyMC-Marketing.
Experience
Roles & impact
Employment
Senior Staff Data Scientist
- Set the technical direction for a supply chain forecasting product optimizing warehouse staff planning, and championed sktime adoption beyond the immediate team, driving uptake across additional teams in the organization
- Introduced sktime to replace a Prophet-only setup, achieving a 10% reduction in offline MAPE across 150+ time series to improve workforce allocation
- Drove the adoption of rigorous engineering standards (unit testing, Ruff, Black) across 40+ data scientists, reducing technical debt and ensuring long-term maintainability
Senior Data Scientist
Oct 2023 – Aug 2024Assortment Optimization
- Built a causal inference framework (S-learner) for assortment optimization, automating product delisting
- Mentored junior and mid-level data scientists
Senior Data Scientist
Jan 2022 – Sep 2023Product Recommender
- Built two real-time recommender systems (two-tower, TensorFlow) trained on billions of transactional rows, deployed on GCP / Vertex AI, driving €10M+ in annual revenue; validated via A/B testing (GrowthBook)
- Led technical hiring: screened 20+ candidates, co-led 5 interviews, onboarded 3 hires
- Mentored 3 data scientists and led upskilling events for a team of 20
Senior Data Scientist
- Developed and open-sourced mamimo, a marketing mix modeling package (74 GitHub stars); results directly influenced marketing budget allocation
- Owned 3 analytical workstreams end-to-end: forecasting (Prophet, LightGBM, QlikSense dashboards), MMM, and causal inference
- Mentored 2 junior data scientists
Data Scientist
- Predicted TV ad view-through rates for media clients: modeled what share of an advertising block audiences would watch
- Joined datasets without a common identifier using statistical record linkage (Nearest Neighbor matching)
- Built the data backend for a competitive benchmarking dashboard, enabling a client to compare performance against direct competitors across key metrics
Trainee Risk
- Automated processes in Risk and Cyber Security (Python, SQL, VBA), eliminating 2 FTE-equivalent manual workloads
Freelance
Guest Instructor & Open-Source Contributor
- Design and deliver the official sktime curriculum across 3 cohorts (15 professionals)
- Active open-source contributor: proposed improvements to StackingForecaster, pipeline caching, and GroupByTransformer
Consulting Data Scientist (Part-Time)
- Part-time advisory and backend engineering (PyMC-Marketing) for a SaaS analytics platform; engagement continued under ScanmarQED after its acquisition of MMMLabs.ai (Feb 2026)
Technical Author
- 90+ articles on data science and ML topics, with 1M+ total reads across platforms
Community
Open source, talks, and volunteering
Open Source
scikit-lego
Regression models for zero-heavy, imbalanced, and uncertain count data.
PyMC-Marketing
Channel contribution attribution charts for marketing mix models.
Talks
Introduction to sktime
- Data science meetup talk covering sktime's forecasting API and pipeline design for practitioners
How we recommend alternatives
- Talk on recommending alternative products when preferred items are unavailable, enabling customers to continue shopping seamlessly
A/B Testing at Metro
- General introduction to A/B testing concepts and experiment design for a business-focused audience
Volunteer
Moderator, matheraum.de
- Supported school and university students with mathematical questions over 12 years
Data Science Mentor
- Guided aspiring data scientists through an end-to-end project predicting used car prices
Writing
Technical articles
Skills
Areas of expertise
Leadership & Vision
Setting direction, growing people
Bridging research and product: defining technical strategy, mentoring engineers, and making complex ideas land with any audience.
ML & Mathematics
Reasoning from first principles
Probabilistic thinking, Bayesian inference, and theoretical foundations applied to real forecasting and decision problems.
Engineering
Building reliable systems
Turning research-grade ideas into production-ready, maintainable systems on modern cloud infrastructure.
Publications
Research papers
My PhD was about attacking a learning problem deliberately designed to be unsolvable. LPN (Learning Parity with Noise) is a machine learning problem engineered to resist efficient algorithms, which is precisely what makes it useful for cryptography. Building dedicated algorithms with precise runtime, memory, and success-probability guarantees developed the tools I reach for in production ML every day: probabilistic reasoning, algorithmic thinking, and comfort with problems that resist easy answers.
A Faster Algorithm for Finding Closest Pairs in Hamming Metric
- Presents a faster randomized algorithm for finding the closest pair of vectors under Hamming distance, with improved complexity for small dimensions relative to dataset size
Time-Memory Trade-offs for the Learning Parity with Noise Problem
- Investigates algorithms that trade memory for runtime when solving the LPN problem, a hard problem underlying several post-quantum cryptographic schemes
Dissection-BKW
- Applies dissection techniques to the BKW algorithm for solving LPN, improving the memory-runtime tradeoff
LPN Decoded
- A unified framework for analysing and comparing LPN solvers, introducing the first quantum algorithm for LPN and demonstrating practical attacks on medium-parameter instances
Education
Academic background
PhD, Mathematics (Cryptography)
- Dissertation: Time-Memory Trade-offs for the Learning Parity with Noise Problem, focusing on memory-efficient algorithms for the cryptographically relevant LPN problem
- 3 peer-reviewed publications (CRYPTO 2017, CRYPTO 2018, FSTTCS 2021)
- Taught cryptography, number theory, discrete mathematics; supervised 5 theses
MSc, Mathematics · Minor: Computer Science
- Grade: 1.2 (top 5%)
Japanese Language & Culture
- Japanese language: A1 → B1
BSc, Mathematics · Minor: Computer Science
- Grade: 1.0 (top 5%)
Recognition
Certifications
Languages
Projects
Open demos
parlascanned
How Bundestag members vote, speak, and earn
Interactive transparency dashboard for the German parliament, built on public disclosure data. Explore voting similarity across 630 MPs, track legislative initiatives, analyze debate topics with NLP, and browse side income by party.
Launch demo →Contact
Get in touch
Open to speaking engagements, consulting, and collaborations around data science, forecasting, and causal inference. Feel free to reach out about anything: articles, open-source work, career questions, or just to say hello. I read everything.