Data Scientist

Job Description — Senior Data Scientist (Experimentation & Causal Inference) Overview

We are seeking an experienced Senior Data Scientist specializing in Experimentation and Causal Inference to lead the design, execution, and analysis of large-scale experimentation initiatives. The ideal candidate will have deep expertise in statistical modeling, A/B testing, causal inference methodologies, and advanced analytics to drive data-informed business decisions. This role partners closely with business leaders, product teams, and engineering organizations to deliver actionable insights that influence strategic direction.

• Lead end-to-end experimentation initiatives, including A/B testing and Geo Experiments, from hypothesis development through execution, analysis, and business recommendations.
• Design statistically rigorous experiments and ensure appropriate experimental methodologies are applied.
• Perform sample size calculations, statistical power analysis, and minimum detectable effect (MDE) estimation.
• Manage multiple testing scenarios while applying appropriate false discovery rate (FDR) control techniques.
• Apply both Bayesian and Frequentist statistical approaches where appropriate.

• Develop and apply advanced causal inference methodologies, including:
• Difference-in-Differences (DiD)
• Propensity Score Matching
• Synthetic Controls
• Instrumental Variables
• Doubly Robust Estimation
• Meta Learners
• Uplift Modeling
• Validate assumptions underlying causal inference techniques and ensure statistical rigor throughout analyses.
• Build predictive and inferential models using regression, classification, time series forecasting, and other advanced statistical techniques.

• Design and develop reusable experimentation and causal inference frameworks that can scale across multiple business domains.
• Build statistical analysis packages and reusable Python/R libraries to improve analytical efficiency.
• Develop production-ready analytical workflows and automation.

• Collaborate with business stakeholders to identify optimization opportunities through experimentation and advanced analytics.
• Translate complex statistical findings into clear, actionable business recommendations.
• Present analytical insights to senior leadership and executive stakeholders in an easily understandable manner.
• Influence strategic decisions through data-driven storytelling.

• Mentor junior data scientists and analysts on statistical methods, experimentation best practices, and scalable solution development.
• Promote a culture of innovation, experimentation, quality, and continuous improvement.
• Lead cross-functional initiatives involving analytics, engineering, and business teams.

• Bachelor’s degree in one of the following disciplines:
• Statistics
• Economics
• Computer Science
• Engineering
• Mathematics
• Physics
• Or another related quantitative field

• Minimum 7 years of experience with a strong focus on experimentation, statistical modeling, and causal inference.

• Regression Analysis
• Classification Models
• Time Series Forecasting
• Experimental Design
• Statistical Inference

Strong expertise in:

• Propensity Score Methods
• Synthetic Controls
• Difference-in-Differences (DiD)
• Instrumental Variables
• Doubly Robust Estimation
• Meta Learners
• Uplift Modeling

• A/B Test Design
• Geo Experiments
• Statistical Power Analysis
• Sample Size Estimation
• Minimum Detectable Effect (MDE)
• Multiple Testing Corrections
• false discovery rate (FDR) Control
• Bayesian Statistics
• Frequentist Statistics

Strong proficiency in:

• Python
• R

• scikit-learn
• LightGBM (LGBM)
• Statistical analysis package development

• Excellent written and verbal communication skills.
• Ability to simplify complex analytical concepts for technical and non-technical audiences.
• Experience presenting recommendations to senior executives.
• Strong stakeholder management and cross-functional collaboration skills.
• Proven ability to independently lead analytical…