Data Scientist

Key Responsibilities

• 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.

Required Qualifications

• Minimum 7+ years of experience with a strong focus on experimentation, statistical modeling, and causal inference.
• Strong expertise in:
• Propensity Score Methods
• Synthetic Controls
• Difference-in-Differences (DiD)
• Instrumental Variables
• Doubly Robust Estimation
• Meta Learners
• Uplift Modeling
• Experimentation A/B Test Design
• Geo Experiments
• Statistical Power Analysis
• Sample Size Estimation
• Multiple Testing Corrections
• False Discovery Rate (FDR) Control
• Bayesian Statistics
• Frequentist Statistics
• Strong proficiency in:
• Python
• R
• Experience With:
• scikit-learn
• LightGBM (LGBM)
• Data engineering
• Etl development
• Data extraction
• Data transformation
• Data integration
• Data quality management
• Data platforms & tools
• 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 initiatives from concept to implementation.