Lead Data Scientist - Research

We are seeking a Senior Quantitative Researcher with over five years of applied quantitative experience and a strong background in statistics, econometrics, or computational finance. This role is focused on addressing complex measurement challenges within a US-based client’s advertising business. It is not a general data science position but requires expertise in framing research questions, conducting rigorous studies, and developing Python code to deliver actionable insights. The work involves measuring the impact of advertising assets on revenue, modeling click-through and ranking dynamics, and continuously evaluating ad strategies to drive evidence-based decisions.

Key Responsibilities

- Translate broad business questions into precise, testable research hypotheses.
- Design studies and experiments tailored to the available data and business context.
- Measure the contribution of individual advertising assets to revenue through attribution, incrementality testing, and marketing mix modeling (MMM/MTA).
- Develop models for click-through rates, ranking dynamics, and market pricing.
- Establish ongoing evaluation frameworks for ad strategies to ensure decisions are data-driven rather than intuitive.
- Collaborate with engineering teams to implement research methods as production monitoring tools.
- Write production-quality Python and SQL code independently, maintaining reproducibility, version control, and code review standards.
- Present complex findings clearly to non-technical stakeholders and confidently defend conclusions under scrutiny.
- Serve as the domain expert for advertising measurement within the team, guiding strategy and best practices.

Required Qualifications

- Master’s or PhD degree in statistics, econometrics, applied mathematics, physics, computational finance, or a related quantitative discipline.
- Minimum of five years’ experience in applied quantitative research.
- Proficiency in Python (including pandas, statsmodels, scikit-learn) and SQL.
- Strong expertise in experimental design and causal inference, with a clear understanding of the limitations of each method.
- Demonstrated independent research capabilities, evidenced by publications, open-source contributions, competition participation, or a professional portfolio.
- Excellent communication skills, with the ability to explain complex statistical results to non-technical leadership effectively.

Preferred Qualifications and Benefits

- Experience in markets, trading, pricing, ad-tech, or actuarial fields.
- Familiarity with time series analysis or Bayesian methods.
- Background in consulting or client-facing roles, with experience delivering research in those environments.

Perks and Benefits

- Gym membership, travel, and food allowances.
- Annual professional certification support.
- Paid time off and holidays.
- Bi-annual performance reviews accompanied by bonus opportunities.

This position offers a unique opportunity to work on challenging measurement problems at the intersection of advertising and quantitative research, contributing directly to strategic decision-making in a dynamic environment.