Lead Data Scientist

Job description

**Lead Data Scientist** Role Overview We are looking for a Lead Data Scientist who is highly hands-on, technically strong, and capable of leading a small team while owning end-to-end delivery of data science solutions. This role requires a balance of deep individual contribution (7080%) and team leadership & stakeholder management (20–30%). You will drive innovation across both classical machine learning and Generative AI initiatives, ensuring solutions move from experimentation to scalable production systems. Key Responsibilities - **End-to-End Solutions**: Design, develop, and deploy data science solutions from problem framing to production. - **Classical ML**: Build and optimize models using regression, classification, clustering, time series forecasting, causal inference, price elasticity modeling, and optimization techniques. - **Generative AI**: Develop and oversee solutions using LLMs, embeddings, RAG pipelines, fine-tuning, and prompt engineering. - **Hands-on Data Work**: Work with SQL and Python for data extraction, analysis, feature engineering, and modeling. - **Leadership**: Lead, mentor, and review work of junior and mid-level data scientists; define AI/ML roadmap aligned with business strategy. - **MLOps & Productionization**: Partner with engineering to deploy models at scale, establish standards for monitoring, retraining, and cost optimization. - **Stakeholder Collaboration**: Work closely with product managers and business leaders to translate problems into measurable outcomes and communicate insights effectively. - **Responsible AI**: Ensure fairness, explainability, compliance, and ethical use of AI models. **Required Skills & Qualifications** - **Experience**: 8–10 years in Data Science/ML/AI roles, with proven business impact. - **Programming**: Advanced proficiency in Python (pandas, numpy, scikit-learn, statsmodels, Hugging Face, PyTorch/TensorFlow) and strong SQL expertise (complex queries, performance tuning). - **ML & AI Expertise**: Solid understanding of statistics, causal modeling, forecasting, optimization, and hands-on experience with LLMs and GenAI techniques. - **Leadership**: Experience leading teams and owning delivery commitments. - **Cloud & Deployment**: Familiarity with cloud platforms (AWS, GCP, Azure) and deploying models into production environments. - **Communication**: Strong problem-solving and ability to explain complex concepts to non-technical stakeholders. **Preferred Qualifications** - **Domain Expertise**: Prior experience in retail, pricing, supply chain, or growth analytics. - **Optimization**: Exposure to PuLP, OR-Tools, Gurobi. - **GenAI Platforms**: Experience with OpenAI, Anthropic, Meta AI, or Google Cloud GenAI APIs. - **Vector Databases**: Familiarity with FAISS, Pinecone, Weaviate, Milvus. - **MLOps Tools**: Experience with MLflow, Kubeflow, Airflow. - **Applications**: Background in NLP, conversational AI, search/recommendation systems, or enterprise GenAI applications. - **Contributions**: Publications or open-source contributions in ML/GenAI.