Lead Data Scientist- Research

We are seeking a Senior Quantitative Researcher with at least five years of applied quantitative experience and a strong background in statistics, econometrics, or computational finance. This position focuses specifically on advanced quantitative challenges within a US-based client’s advertising business and is not a general data science role. The successful candidate will be responsible for framing complex measurement questions, designing rigorous studies, and developing Python code to deliver actionable insights.

Key Responsibilities

- Research Design: Translate broad business questions into precise, testable research hypotheses. Design studies and experiments tailored to the available data and select appropriate analytical methods.

- Measurement & Modelling: Evaluate how individual advertising assets contribute to revenue using attribution techniques, incrementality testing, and marketing mix modeling (MMM/MTA). Develop models to understand click-through rates, ranking dynamics, and market pricing.

- Continuous Evaluation: Establish ongoing evaluation frameworks for advertising strategies to ensure decisions are data-driven rather than instinctual. Collaborate with engineering teams to operationalize research methods into production monitoring systems.

- Hands-on Analysis: Write production-quality Python and SQL code independently, ensuring that analyses are reproducible, version-controlled, and subject to peer review without relying on intermediaries.

- Communication & Domain Ownership: Present complex findings clearly to non-technical stakeholders and confidently defend conclusions under scrutiny. Serve as the subject matter expert for advertising measurement within the team.

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 capability, evidenced by publications, open-source contributions, competition participation, or a professional portfolio.

- Excellent communication skills with the ability to translate statistical results for a non-technical audience.

Preferred Qualifications and Benefits

- Experience in markets, trading, pricing, ad-tech, or actuarial domains.

- Familiarity with time series analysis or Bayesian methods.

- Background in consulting or client-facing delivery environments.

- Benefits include gym, travel, and food allowances, annual certification support, paid time off and holidays, and bi-annual performance reviews with bonuses.

This role offers a unique opportunity to work at the intersection of quantitative research and advertising strategy, driving measurable impact through rigorous analysis and innovative modeling.