Senior Data Scientist
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- Experimentation Excellence: Lead the design and analysis of A/B tests with high statistical standards. You will not just calculate p-values, but rigorously check for Sample Ratio Mismatch (SRM), Novelty/Primacy effects, and biases to ensure validity before rollout.
- Diagnostic & Root Cause Analysis: Proactively investigate sudden drops in core metrics (like Retention or Conversion). You will differentiate between technical issues (bugs, latency) and market behaviors/competitor actions, and quantify the GMV loss to prioritize engineering fixes.
- Economic Strategy: Analyze price elasticity and demand curves to recommend optimal pricing points. You will balance trade-offs between Conversion Rate lift and Operational Costs (e.g., support tickets, refunds) to maximize long-term profitability.
- Advanced Data Processing: Write high-performance SQL for complex cohort analysis and data aggregation. You will develop optimized Python scripts (using vectorization instead of loops) to handle large-scale datasets (10M+ rows) efficiently.
- Strategic Partnership: Act as a thought partner for Product Managers. You will challenge assumptions with data, translating complex statistical concepts (Welch’s t-test, confidence intervals) into actionable business recommendations.
- Automation & Innovation: (Optional but valued) Design workflows using AI Agents (N8N, LLMs) or Python scripts to automate anomaly detection and metric monitoring.
- Educational Background: Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field.
- Experience: 3+ years in Product Analytics, Data Science, or Growth, with a specific focus on A/B Testing and causal inference.
Perfil Tech
- Technical Stack:
- SQL Expert: Ability to write complex window functions, manage sparsity in cohort retention, and prepare data for statistical testing.
- Python Proficiency: Strong skills in Pandas/Polars/NumPy. You understand computational complexity ($O(N^2)$ vs $O(N)$) and know how to vectorize operations to optimize performance.
- Business Acumen: Ability to calculate the financial impact of technical decisions. You understand concepts like LTV, CAC, and Price Elasticity of Demand.
- Communication: Demonstrated ability to explain "why" a test failed or succeeded to non-technical stakeholders, moving beyond the numbers to the strategic implication.
- Plus: Experience with Workflow Automation (N8N, Airflow) or building simple RAG/AI agents for internal tools.
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