4 weeks ago

Data Scientist II

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Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Partner with Product, Marketing, and Finance to deliver product analytics, experimentation, and causal inference for edX.org’s learning marketplace. Triage high volumes of stakeholder questions, define metrics and north-star KPIs, and build dashboards and tooling. Own A/B tests end-to-end and apply quasi-experimental methods when needed. Contribute dbt models, validate data quality with Data Engineering, and use machine learning to turn complex behavior datasets into actionable recommendations.

Location: United States

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Serve as the analytics partner for Product, Marketing, and Finance on edX.org, delivering funnel, cohort, retention, and segmentation work.
•Triage a high volume of incoming requests and match response rigor to the decision at hand.
•Own A/B tests end-to-end, and apply quasi-experimental methods (difference-in-differences, propensity scoring, regression discontinuity, synthetic control) when randomization isn’t possible.
•Define north-star, input, and guardrail KPIs and build scalable dashboards and tooling to reduce repeat requests.
•Partner with Data Engineering, build/maintain dbt models, lead EDA on unfamiliar datasets, and add validation checks to ensure data quality.

Pay and Benefits

Salary: USD 107,300 - 124,200 annually

Perks:Health InsuranceDentalVisionLife InsuranceDisability401kPaid LeaveParental Leave

Key Requirements

•Bachelor’s degree in a quantitative field (data science, statistics, mathematics, computer science, economics, or similar).
•3–5 years in data science, product analytics, or a related quantitative role.
•Strong statistics foundations including hypothesis testing, power analysis, confidence intervals, and multiple comparisons, plus solid EDA and validation skills.
•Experience running A/B tests end-to-end and using quasi-experimental methods (difference-in-differences, propensity scoring, regression discontinuity, synthetic control).
•Strong Python (pandas, NumPy, scikit-learn) and advanced SQL on large, complex datasets, with experience using platforms like Snowflake/BigQuery/Databricks/Spark (preferred).

Experience:3-5 yearsProduct analyticsExperimentationCausal inferenceEdTechOnline learning

Education:Bachelor's

Skills:CommunicationIntellectual curiosityInitiativeStakeholder enablementProblem triage

Tech Stack:PythonPandasNumPyScikit-learnSQLDbtAmplitudeFullStoryMixpanelSnowflakeBigQueryDatabricksSparkStatsigEppoOptimizelyLaunchDarkly

Company Brief

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