We’re looking for a Data Science & Analytics Lead who is responsible for leading and growing a high-performing team of data scientists and analysts, establishing rigorous statistical and modeling standards, spearheading causal inference and experimentation frameworks, and translating complex transactional data into strategic business impact.

This role requires strong technical mentorship, deep expertise in econometrics, experimentation, and applied machine learning, practical data storytelling for executive leadership, and the discipline to manage team capacity while protecting deep technical work.

Key Responsibilities

• Team Leadership & Capacity Management: Lead, mentor, and expand the data science and analytics team; run hiring, onboarding, and performance reviews while acting as the single broker for business requests to safeguard deep work.
• Engineering & Technical Standards: Institute best practices for code reviews, reproducible pipelines/notebooks, robust validation methodologies, and transparent documentation of model assumptions and limitations.
• Experimentation & Causal Inference: Design and evaluate randomized experiments (A/B testing, dose-response) with power and MDE calculations; apply quasi-experimental methods (DiD, synthetic controls, bunching) to measure true incremental ROI versus subsidized value.
• Production Machine Learning & Uplift Modeling: Own production-grade models (retailer targeting, recommendations, churn, demand forecasting, credit scoring) and transition from standard correlations toward heterogeneous treatment effect (CATE/uplift) models evaluated on incremental NMV and business metrics.
• Core Metrics & Decision Support: Define and maintain foundational business metrics (retention, share of wallet, basket dynamics, cohort funnels), building reliable self-serve dashboards to reduce recurring ad-hoc requests.
• Executive Communication: Synthesize complex analytical findings into clear, actionable trade-offs and recommendations for C-level leadership (CTO, CPO), communicating uncertainty and confidence boundaries transparently.

Qualifications & Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, Economics, Mathematics, Data Science, or a related quantitative field.
• 5+ years of hands-on experience in Data Science or Advanced Analytics, including at least 1–2 years leading people or owning an analytics roadmap.
• Strong command of applied statistics and causal inference (experimentation design, statistical power, difference-in-differences, quasi-experiments, confounding detection).
• Proven applied ML capabilities (gradient boosting, classification, ranking, recommendation systems) with strict leakage control and validation hygiene.
• Expert-level SQL (PostgreSQL) dealing with messy line-item transactional data, alongside deep proficiency in Python (pandas, scikit-learn, statsmodels).
• Exceptional communication skills in Persian and English, with a demonstrated ability to translate business ambiguity into structured technical solutions.
• High autonomy and firmness in scope negotiation and prioritization under competing business demands.

Preferred Qualifications (Nice to Have)

• Direct domain experience in B2B e-commerce, retail, FMCG, or distribution ecosystems.
• Advanced experience in Uplift / CATE modeling (causal forests, Double Machine Learning, meta-learners) and modeling zero-inflated or heavy-tailed revenue distributions.
• Experience in promotional incentive optimization, tiered cashback structures, dynamic pricing, or credit risk evaluation.
• Hands-on experience architecting BI data products (Metabase, Superset, Power BI) and working with ERP schemas (e.g., SAP).
• Familiarity with LLM-assisted workflows to accelerate data pipelines and analytical discovery responsibly.