Liftoff is looking to solve the problem of transforming complex data into rigorous analysis to directly shape product strategy and machine learning development for its AI-powered performance marketing platform.
Requirements
- 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).
- Experience with BI and visualization tools (e.g., Looker, Hex, Tableau, Databricks) to enable transparency and self-service reporting.
- 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).
- Ability to translate complex analyses into clear, actionable recommendations for diverse audiences ranging from engineers to business leaders.
Responsibilities
- 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.
Other
- Degree in a quantitative field (e.g., Computer Science, Statistics, Economics, Engineering, or related). Advanced degree preferred; equivalent practical experience accepted.
- Travel expectations: attend in-person team gatherings at least once per quarter.
- 3+ years of experience in product, business, or data analysis in data-driven environments (e.g., ad-tech, mobile, SaaS).
- 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.
