Data Scientist

About the Role We are looking for a Senior Data Scientist with strong expertise in building and deploying production-grade Machine Learning systems . The ideal candidate should have deep hands-on experience in Traditional Machine Learning, MLOps, statistical modeling, and feature engineering , along with a proven ability to deliver scalable solutions that drive measurable business impact in regulated domains. Key Responsibilities Design, develop, and deploy end-to-end Machine Learning models using Python, Scikit-learn, XGBoost, and other advanced algorithms. Build robust data pipelines, feature stores, and production ML systems with a strong focus on performance, scalability, and reliability. Implement MLOps practices including experiment tracking (MLflow), model versioning, CI/CD pipelines, Docker, Kubernetes, model monitoring, and drift detection. Apply statistical modeling, causal inference, and experimentation techniques (A/B testing, uplift modeling) to solve complex business problems. Collaborate closely with business stakeholders to understand requirements and translate them into high-impact ML solutions. Ensure model governance, explainability (SHAP/LIME), and compliance in regulated environments. Mentor junior data scientists and contribute to building reusable frameworks and best practices. Must-Have Skills CategorySkillsPriority Traditional ML XGBoost, Random Forest, Logistic Regression, Feature Engineering, Handling Imbalanced Data, Model Evaluation, Cross-Validation, Hyperparameter TuningVery High MLOps & Deployment MLflow, Docker, Kubernetes, CI/CD, Model Monitoring, Data Drift Detection, Model GovernanceVery High Business Impact Proven experience in delivering ML solutions with quantifiable business outcomes Very High Domain Experience Experience in Banking, Insurance, or Pharmaceutical domainsHigh Programming Python (Pandas, NumPy, Scikit-learn), SQL, PySparkHigh Good-to-Have Skills Experience with Generative AI and Agentic AI (RAG, LangChain, LangGraph, Vector Databases, Multi-Agent Systems) Hands-on experience with Causal Inference techniques (Propensity Score Matching, Difference-in-Differences) Strong knowledge of Explainable AI (SHAP, LIME) Experience with cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) Exposure to LLM fine-tuning (LoRA/QLoRA) or RAG evaluation frameworks (e.g., RAGAS) Qualifications Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field. 4+ years of hands-on experience in Data Science / Machine Learning, with a strong focus on production ML systems . Demonstrated ability to deliver business-impacting solutions with measurable ROI. Excellent problem-solving, analytical, and stakeholder communication skills.