Data Scientist, Product Analytics

Liftoff Mobile

full-time

Posted on: 9/18/2025

Origin: • 🇺🇸 United States • California, Colorado, Florida, Idaho, Illinois

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💰 $150,500 - $195,000 per year

Mid-LevelSenior

BigQueryNumpyPandasPythonScikit-LearnSparkSQLTableau

About the role

Define key success metrics in collaboration with Product and Engineering teams; ensure metrics are statistically sound and consistently measured.
Design and conduct A/B tests and other experiments to validate product hypotheses with statistical rigor, providing objective assessments of product impact.
Apply advanced statistical and machine learning methods to uncover performance drivers/bottlenecks, and help inform the direction of Liftoff’s ML development.
Develop scalable analytical frameworks and reusable code to streamline recurring analyses and improve internal data models.
Productionize deep analytics work into user-facing dashboards, applications, and data products that enable self-service insights and scalable performance monitoring across teams.
Collaborate with Product, Data Engineering, and ML teams to evaluate new features, improve modeling frameworks, and continuously iterate on ML performance.
Investigate anomalies and trends in product or business performance and present clear, actionable insights.
Mentor analysts, foster a culture of data literacy, and set standards for analytical rigor both within the Product Analytics function and across the organization.
Document methodologies, models, and workflows to ensure reproducibility, transparency, and institutional knowledge.

3+ years of experience in product, business, or data analysis in data-driven environments (e.g., ad-tech, mobile, SaaS).
Degree in a quantitative field (e.g., Computer Science, Statistics, Economics, Engineering, or related). Advanced degree preferred; equivalent practical experience accepted.
Expertise in SQL with the ability to query, transform, and optimize large datasets across distributed query engines (e.g., Trino, Spark, BigQuery).
Proficiency in Python and the data science ecosystem (Pandas, NumPy, SciPy, scikit-learn, statsmodels, Jupyter), with hands-on experience conducting exploratory and statistical analyses.
Strong foundation in statistical methods and experimental design (e.g., hypothesis testing, regression analysis, causal inference, A/B testing frameworks).
Ability to translate complex analyses into clear, actionable recommendations for diverse audiences ranging from engineers to business leaders.
Experience with BI and visualization tools (e.g., Looker, Hex, Tableau, Databricks) to enable transparency and self-service reporting.