Job Duties: Deep strategic analysis to answer core business and operational questions including assessing the trade-off between metrics change, evaluating overall impact of changes of ads ecosystem. Write clear, actionable data and business analyses that help teams identify areas of improvement and investment. Build segmentation models to assess supply to inform pricing strategy. Improve decision velocity and quality using data scientist tool kit as well as experimentation, causal inference techniques. Design measurement strategy, advise on experimentation best practices, identifying flaws in experiment practices and results including building tools for experiment analysis. Identify the right measures of success for engineering teams and help them track those metrics. Break down high-level metrics into actionable segments, including spanning from collecting entirely new datasets to building dashboards to track components of a metric (e.g., monitoring conversion data for missing values, implausible values, duplicated data). Telecommuting and/or remote employment permitted.

 Minimum Requirements: Master's degree (or a foreign equivalent) in Finance, Data Science, or a related field and three (3) years of experience in the job offered or in a related position.

 Special Skill Requirements: Must have at least three (3) years of experience in each of the following:

1. Advanced SQL proficiency for large-scale data analysis in distributed data warehouse environments such as Presto, Spark SQL, and Hive.
2. Strong Python proficiency for large-scale data analysis and modeling, including the use of pandas, NumPy, stats models, and scikit-learn, as well as building robust analysis pipelines and production-ready notebooks or scripts.
3. Deep expertise in applied machine learning and algorithmic modeling, including model development, evaluation, and optimization for real-world product use cases.
4. Expertise in designing and analyzing online experiments for product changes, including power analysis, variance reduction, guardrail design, and heterogeneous treatment effect analysis on key metrics.
5. Ability to structure ambiguous product questions into clear analytical roadmaps and deliver recommendations with quantified impact, risks, and assumptions.
6. Expertise in causal inference for observational analyses, including methods such as propensity score matching/weighting and difference-in-differences, to estimate incremental impact and control for confounding factors.
7. Experience defining and governing metrics and instrumentation, including event taxonomy, deduplication rules, attribution windows, metric specifications, data contracts, and lineage, to ensure consistency and reliability.
8. Experience building scalable dashboards and automated insight-generation workflows to monitor core metrics, surface anomalies, and deliver stakeholder-ready insights for cross-functional partners.
9. Experience conducting funnel and ecosystem analyses to identify bottlenecks, quantify trade-offs, and prioritize high-leverage product opportunities.
10. Experience performing segmentation and cohort analyses to understand differences in engagement and retention, and to inform targeted interventions.
11. Experience building clustering and segmentation models to identify meaningful user segments and usage scenarios.
12. Strong statistical modeling and inference skills to quantify effects, measure uncertainty, and communicate statistical significance appropriately.

Telecommuting and/or remote employment permitted.

 Salary: $226,089.00 - $287,749.00 per annum.

Reference #: L25-172716

This position is not available for relocation assistance.