Job description
Join Lumina Analytics as a Senior Statistician based in New York, where you'll apply rigorous statistical methods to real-world data and influence product decisions. This role blends theoretical expertise with practical impact, offering the chance to shape data-driven strategies across multiple domains.
We’re seeking a collaborative problem-solver who can translate complex analyses into actionable insights for cross-functional teams, from product to engineering and leadership.
As a member of the Statistics & Data Science team, you’ll lead modeling initiatives, design experiments, and help scale statistical solutions across the organization, ensuring reproducibility and high standards of rigor.
Responsibility
- Lead end-to-end statistical analyses on large-scale datasets to inform product strategy and policy decisions.
- Develop, validate, and maintain predictive models (regression, classification, time-series, Bayesian approaches).
- Design and oversee experimentation (A/B/n tests), quasi-experimental designs, and causal inference studies.
- Collaborate with data engineers and software teams to deploy models into production and monitor performance.
- Communicate findings to stakeholders through clear reports, dashboards, and presentations.
- Ensure reproducible research practices, version control, and thorough documentation.
- Mentor junior statisticians and foster best practices in statistical rigor.
Qualification
- Master’s or PhD in Statistics, Biostatistics, Mathematics, or a closely related field.
- 5+ years of applied statistics experience in industry or research settings.
- Strong proficiency in R and Python; experience with SQL and data visualization tools (e.g., ggplot2, seaborn, Plotly).
- Experience with machine learning methods and experimental design, including A/B testing and causal inference.
- Excellent communication skills with the ability to explain complex methods to non-technical audiences.
- Proven ability to translate business questions into statistical frameworks and deliver actionable insights.
- Familiarity with data pipelines, reproducible research practices, and version control (Git).