The opportunity
At 2U, we are all in on purpose. We are motivated by our mission – to make learning limitless– and connected by our shared passion to deliver world-class higher education at scale.
What you'll do
Product analytics and ad hoc analysis Serve as the analytics partner for: Product, Marketing, and Finance teams on edX.org, delivering funnel, cohort, retention, and segmentation work. Triage a high volume of incoming requests and match the depth of each response to the decision at hand, from back-of-the-envelope estimates to deeper investigations.
Experimentation and causal inference Own A/B tests end-to-end, from design: and power analysis through interpretation. Where randomization isn't possible, use quasi-experimental methods such as difference-in-differences, propensity scoring, regression discontinuity, and synthetic control.
Metric definition and self-serve enablement Establish and maintain: north-star, input, and guardrail KPIs. Build scalable dashboards and tooling that reduce the long tail of repeat requests.
Data modeling and validation Partner with Data Engineering to keep upstream: sources trustworthy. Contribute to dbt models, lead EDA on unfamiliar datasets, and add checks that catch quality issues before they reach dashboards or partners.
Applied machine learning Use standard ML techniques (classification,: regression, clustering) where they accelerate insight beyond what statistical analysis alone can deliver.
Domain learning and stakeholder education Develop a deep understanding of the: edX product, learners, and business. Coach partners on statistical concepts, refine ambiguous questions, and turn complex findings into actionable recommendations.
What they're looking for
- Education: Bachelor's degree in a quantitative field (data science,: statistics, mathematics, computer science, economics, or similar). Graduate degree preferred.
- Professional Experience: 3–5 years in data science, product analytics, or a related quantitative role
- Communication: Able to refine ambiguous questions, translate findings for business partners, and clarify what the results do and don't say.
- Statistics & data rigor: Solid foundation in hypothesis testing, power analysis, confidence intervals, and multiple comparisons, plus strong EDA and validation skills on unfamiliar data
