Senior Lead Data Scientist

## About the Role We are looking for a Senior / Lead Data Scientist with 6–9 years of hands-on experience to drive end-to-end data science initiatives — from problem framing and experimentation to deploying production-grade ML solutions. You will lead a team of data scientists, partner with engineering and product stakeholders, and own the technical roadmap for advanced analytics and machine learning across the organization. ## Key Responsibilities - Lead the design, development, and deployment of machine learning and statistical models to solve high-impact business problems. - Own the full ML lifecycle: data exploration, feature engineering, model development, validation, deployment, monitoring, and retraining. - Mentor and guide a team of 3–6 data scientists; conduct code/model reviews and set best practices. - Collaborate with product managers, data engineers, and business stakeholders to translate ambiguous business problems into well-defined data science solutions. - Build and productionize ML pipelines in partnership with ML engineering teams (MLOps). - Drive experimentation frameworks (A/B testing, causal inference) and define success metrics. - Develop and fine-tune Generative AI / LLM-based solutions (RAG pipelines, prompt engineering, model fine-tuning) where applicable. - Communicate insights and model outcomes to senior leadership through clear storytelling and visualization. - Stay current with the latest research and evaluate new tools, techniques, and frameworks for adoption. - Ensure model governance: fairness, explainability, reproducibility, and compliance. ## Required Technical Skills ### Programming & Tools - Expert-level Python (NumPy, Pandas, Scikit-learn, statsmodels); working knowledge of R is a plus - Strong SQL (complex queries, window functions, query optimization) - Version control with Git/GitHub/GitLab; comfort with CI/CD workflows ### Machine Learning & Statistics - Supervised/unsupervised learning: regression, classification, clustering, ensemble methods (XGBoost, LightGBM, CatBoost, Random Forests) - Strong foundation in statistics: hypothesis testing, Bayesian methods, time-series forecasting (ARIMA, Prophet), causal inference, A/B experimentation - Model evaluation, hyperparameter tuning (Optuna, GridSearch), and handling imbalanced data ### Deep Learning & NLP - Hands-on with TensorFlow / PyTorch / Keras - NLP: transformers, Hugging Face, embeddings, text classification, NER - Computer vision experience (CNNs, object detection) is a plus ### Generative AI / LLMs - Experience with LLMs (GPT, Claude, Llama, Gemini), prompt engineering, and fine-tuning (LoRA/PEFT) - Building RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, ChromaDB) - Frameworks: LangChain, LlamaIndex ### Big Data & Data Engineering - Distributed computing: Apache Spark / PySpark, Databricks - Data warehouses: Snowflake, BigQuery, Redshift - Workflow orchestration: Airflow, Prefect, dbt - Streaming (Kafka) exposure is a plus ### MLOps & Deployment - Model deployment: Docker, Kubernetes, FastAPI/Flask, REST APIs - ML platforms: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML - Model monitoring, drift detection, and retraining pipelines ### Cloud Platforms - Strong experience with at least one: AWS / GCP / Azure (compute, storage, ML services) ### Visualization & BI - Tableau / Power BI / Looker, Plotly, Matplotlib, Seaborn - Dashboarding and executive-level storytelling ## Required Qualifications - Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field (PhD a plus) - 6–9 years of industry experience in data science, with at least 2 years in a senior or lead capacity - Proven track record of deploying ML models to production with measurable business impact - Experience mentoring junior data scientists and leading cross-functional projects ## Preferred / Nice-to-Have - Publications, patents, Kaggle achievements, or open-source contributions - Domain experience in [e-commerce / fintech / healthcare / SaaS — customize] - Experience with recommendation systems, fraud detection, or demand forecasting - Familiarity with data privacy regulations (GDPR, etc.) and responsible AI practices ## Soft Skills - Strong stakeholder management and executive communication - Ability to translate business problems into analytical solutions - Structured problem-solving, ownership mindset, and bias for action - Team leadership, mentoring, and conflict resolution ## What We Offer - Competitive salary + performance bonus + ESOPs (customize) - Flexible/hybrid working model - Learning & development budget, conference sponsorships - Health insurance and wellness benefits