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# Data Scientist II

- **Company:** 2U
- **Location:** Washington, DC, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $107,300.0 - $124,200.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Big Data, BigQuery, Computer Programming, Information Engineering, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Mixpanel, Usage Analysis, Snowflake, Apache Spark, Pandas, Scikit Learn, Information Technology, Power Analysis (Cryptography), Optimizely, Databricks
- **Published:** May 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e072650f295cc478

## About the Role

Do you have experience in Spark?, Do you have a Bachelor's degree?, The Data Scientist II will partner with Product, Marketing, and Finance stakeholders to drive insight and decision-making for edX.org, 2U's open online learning marketplace.

This is a high-leverage analytics role focused on product analytics, experimentation, and causal inference on large, complex behavioral datasets. We are looking for someone who is excited by ambiguous business problems, can triage a high volume of stakeholder requests and match each one to an appropriate level of rigor, and is skilled at turning messy data into clear, actionable insight.

The ideal candidate is intellectually curious, takes the initiative to understand the domain and refine the question behind the question, and communicates complex findings in ways that educate and enable stakeholders., * 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
 * Experimentation & causal inference: Running A/B tests end-to-end, plus quasi-experimental methods where randomization isn't possible (e.g., difference-in-differences, propensity scoring, regression discontinuity, synthetic control). Prefer experience with modern experimentation platforms (e.g., Statsig, Eppo, Optimizely, LaunchDarkly).
 * Product analytics & domain: Hands-on with platforms like Amplitude, FullStory, or Mixpanel, plus prior work in EdTech, online learning, or a comparable consumer subscription / marketplace product
 * Programming & data: Strong Python (pandas, NumPy, scikit-learn) and advanced SQL on large, complex datasets. Prefer exposure to large-scale data platforms (e.g., Snowflake, BigQuery, Databricks, Spark).

While this position is open to remote candidates across the U.S., we will prioritize those who live in the Washington-Baltimore metropolitan area and who are available to come into our Headquarters in Arlington, VA two days a week.

## Description

* 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.


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