Product Data Scientist

Infinitas Learning; Utrecht; Hybrid; EUR 70,000 - 100,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Infinitas Learning seeks a Product Data Scientist to raise evaluation standards for impact across our digital learning platform. You will design experiments, build robust analytical models, and translate results into clear, actionable guidance for product teams.

You will collaborate with engineers and product stakeholders to ensure data quality and credible insights that scale across learner and teacher experiences, with a hybrid work pattern in Utrecht.

Qualifications

• 3+ years of experience in statistical modelling or related field.
• Strong SQL and fluent Python or R for analysis and modelling.
• Experience in experimentation, design, power analysis, and causal inference.

Responsibilities

• Design A/B tests and quasi-experimental methods when needed.
• Perform statistical modelling to validate insights and drive decisions.
• Build the evidence base showing learning outcomes on our platform.

Skills

SQL

Python or R

Statistical modelling

Experimentation

Causal inference

Multilevel models

Product analytics

Communication with stakeholders

Tools

Power BI

Mixpanel

Job description

Purpose

The Product Data Scientist raises the standard of how we measure and prove impact across our digital learning platform. You set the bar for trustworthy metrics, valid comparisons, and sound evidence and use it to answer the questions that shape our product. The scope covers learner and teacher experiences across our Learning Platforms. Becoming genuinely data-driven is a core pillar for our company, and this role is important in reaching that goal.

Key responsibilities

Experimentation and causal evaluation
Design A/B tests and, where controlled testing isn't possible (seasonality, classroom-locked cohorts, staged rollouts), apply quasi-experimental methods such as difference-in-differences, matching, and interrupted time series. Power analysis and minimum detectable effect up front; disciplined handling of peeking, multiple comparisons, and variance reduction.

Statistical modelling for validation and insight
Look beyond single metrics and simple comparisons. Use models to find what really drives an outcome, to account for the fact that users sit within groups such as classes and schools, to validate our data, and to test assumptions with forward-looking estimates. This is modelling to understand and validate not to build live machine learning.

Learning outcomes and efficacy
Build the evidence base that learners make progress on our platforms. Design A/B tests and comparison-group analyses that test whether product changes improve learning, accounting for prior ability and teacher and school effects.

Success metrics: design and validation
Define success metrics and guardrails for product initiatives, and validate them, does the metric measure what we claim, is it sensitive enough to detect real change, does it hold up over time and across segments. Own the definitions that everyone else builds on.

Analytical standards and judgement
Set the standards for how analysis is done here: unit of analysis, when numbers may be aggregated and when they may not, weighting, comparability across products and opcos, and how uncertainty is communicated. Review the work of others and raise the bar through that review.

AI-assisted analysis and analytical agents
Work with engineers to build and evaluate agents that support analysis (automated experiment readouts, anomaly detection, querying over certified data models). Own the evaluation side and analysis guardrails: define how we know an agent's answer is correct before we trust it at scale.

Measurement infrastructure and data quality
Co-own tracking and instrumentation plans with Engineering. Ensure the metrics we depend on are accurate, documented, monitored, and traceable.

Communication and decision impact
Turn analysis into short, sharp narratives with a clear recommendation and honest trade-offs. Be equally willing to say what the data supports and what it cannot answer.

Requirements

Must-have

• At least 3 year’s experience in statistical modelling or machine learning
• Strong SQL and fluent Python or R for analysis and modelling
• Experience in experimentation: experimental design, hypothesis and metric definition, power analysis, statistical testing, and causal inference where randomisation isn't available
• Solid applied statistics: regression and multilevel models, correlation structures, comfort reasoning about uncertainty and confounding
• Product analytics experience (web/app/platform): funnels, cohorts, retention, segmentation, adoption
• Data quality mindset: instrumentation design, schema discipline, documentation, testing and monitoring
• Communication: concise memos and visuals that drive decisions; credible with senior stakeholders
• Stakeholder skills: effective partnership with PM, Design and Engineering

Nice to have

• Experience with BI and product analytics tooling (Power BI, Mixpanel or similar)
• Survey and attitudinal measurement methodology (sample sizing, CSAT interpretation)
• Nice‑to‑have: educational measurement, psychometrics, IRT, learning analytics, or a background in educational/behavioural research methods

Team & practical

You’ll join the data team within our product organisation, alongside two product analysts, reporting to the Head of Analytics. You’ll work hybrid, with regular days at our Utrecht office.