Data Scientist (Monetization & Pricing)

Experience: 4-6 Years

Industry: E-commerce / Marketplace / Fintech

Role Summary:

- We are seeking a high-caliber Data Scientist to join our Monetization team. In this role, you will act as a "Pricing & Measurement Architect," responsible for establishing the core modeling foundations for pricing, fees, and promotional incentives.

- You will bridge the gap between complex economic theories and scalable engineering, building elasticity libraries to quantify demand response and measuring substitution/cannibalization effects across products.

- The ideal candidate possesses advanced SQL and Python skills, a deep understanding of Causal Inference, and the ability to translate ambiguous business questions into crisp, measurable analytical plans.

Responsibilities:

- Foundational Monetization Modeling: Build and maintain advanced ML models to quantify price elasticity, demand response, and the impact of promotional incentives.

- Causal Inference & Experimentation: Apply advanced experimentation techniques in a product setting, including power analysis, primary/secondary metric design, and hypothesis testing to drive monetization strategy.

- Cannibalization & Substitution Measurement: Develop frameworks to quantify and explain how different products, offers, and promotion types interact and compete within the ecosystem.

- Monetization Optimization: Utilize optimization methods and solvers (e.g., linear programming, constrained optimization) to design efficient fee structures and promotional budgets.

- Advanced Analytics Workflows: Write production-quality SQL and use Python to automate complex data processing, modeling, and analytical workflows.

- Self-Service Intelligence: Build and own complex Tableau dashboards and the supporting data pipelines to enable scalable tracking of monetization KPIs.

- LLM Integration for Analytics: Explore and apply Large Language Models (LLMs) to accelerate insight generation, automate reporting, and augment analytical workflows.

- Root-Cause & Structured Problem Solving: Perform deep-dive analyses to identify the drivers of business performance, translating ambiguous stakeholder questions into actionable metrics.

- Stakeholder Storytelling: Communicate complex technical insights and modeling outcomes clearly to both technical peers and non-technical business leadership in fluent English.

Technical Requirements:

- Programming & SQL Mastery: Proven ability to write efficient, production-grade SQL and Python for data science applications.

- Statistical Rigor: 4+ years of experience in structured problem solving, root-cause analysis, and advanced causal inference.

- Machine Learning Profile: Strong orientation in baseline predictive modeling, specifically applied to monetization or pricing analytics.

- Experimentation Excellence: Hands-on experience designing and interpreting complex A/B tests in a fast-paced product environment.

Preferred Skills:

- Elasticity Modeling: Ability to build and maintain a library of demand/supply response models.

- Substitution Analysis: Experience in quantifying cross-product substitution and promotional cannibalization.

- Optimization Methods: Exposure to linear programming or constrained optimization for business design.

- Advanced Visualization: Mastery of Tableau for building complex, self-service analytical tools.

- AI Innovation: Exposure to applying LLMs for analytics acceleration and automation.

Core Competencies:

- Business Acumen: Ability to align technical depth with monetization goals and revenue growth.

- Clarity of Thought: Excellence in translating ambiguous business requirements into structured analytical roadmaps.

- Technical Depth: A commitment to building foundational frameworks that are scalable and reproducible.

- Result Driven: A focus on delivering measurable impact through pricing optimization and fee design