Data Scientist - Research

We are hiring a Senior Quantitative Researcher with 5+ years of applied quantitative experience and a background in statistics, econometrics or computational finance. This is not a general data science role.

The role involves the hardest measurement questions in a US-based client's advertising business: framing the question, running a rigorous study, and writing the Python to answer it.

Day to day, this means measuring how advertising assets contribute to revenue, modelling click-through and ranking dynamics, and building continuous evaluation of ad strategy.

Scope of Work:

Research Design

• Turn loose business questions into testable research questions.
• Design studies and experiments, and choose methods that fit the data available.

Measurement & Modelling

• Measure how individual advertising assets contribute to revenue through attribution, incrementality testing and marketing mix modelling (MMM/MTA).
• Model click-through and ranking dynamics, and market pricing.

Continuous Evaluation

• Set up ongoing evaluation of ad strategy so decisions rest on evidence, not instinct.
• Work with engineers to turn research methods into production monitors.

Hands-on Analysis

• Write production-quality Python and SQL directly, with no handoff to an analyst.
• Keep analysis reproducible, versioned and reviewable.

Communication & Domain Ownership

• Present findings to non-technical audience and defend conclusions under hard questions.
• Act as the domain owner for advertising measurement across the team.

Must Haves:

• Master's or PhD in statistics, econometrics, applied mathematics, physics, computational finance or a similar quantitative field.
• 5+ years of applied quantitative work.
• Strong Python (pandas, stats models, scikit-learn) and SQL.
• Solid experiment design and causal inference, including where each method breaks down.
• Evidence of independent research: papers, open-source contributions, competition work or a portfolio.
• Excellent communication, with the ability to explain statistical results to non-technical leaders.

Good to Have:

• Experience in markets, trading, pricing, ad-tech or actuarial work.
• Time series or Bayesian methods.
• Experience working in a consulting or client-facing delivery environment.

Perks & Benefits:

• Gym, travel and food allowance
• Annual certification
• Paid time off and holidays
• Bi-annual performance reviews and bonuses