Lead - Data Scientist

Role & Responsibilities: - Lead the end-to-end lifecycle of data science and machine learning projects, from problem definition and data exploration through model development, validation, deployment, and monitoring. - Translate complex business problems into data-driven, statistical, and machine learning solutions with clearly defined success metrics. - Design and develop predictive, classification, recommendation, forecasting, optimization, NLP, or GenAI solutions based on business requirements. - Establish and follow best practices for MLOps, model versioning, deployment, monitoring, retraining, and model governance. - Leverage cloud platforms such as AWS, Azure, or GCP and relevant data/ML platforms. - Evaluate emerging technologies in Generative AI, LLMs, NLP, deep learning, and agentic AI, and identify opportunities for business adoption. - Define technical approaches, architecture, experimentation strategies, and project roadmaps for complex AI initiatives. - Monitor production models for accuracy, data/model drift, bias, latency, reliability, and business impact, and drive continuous improvement. - Lead technical discussions, conduct code/model reviews, and establish standards for data science quality and documentation. - Mentor and coach data scientists, helping build technical capabilities and improving engineering and analytical practices across the team. - Collaborate with senior business stakeholders to communicate insights, model outcomes, risks, and recommendations in a clear and actionable manner. - Manage multiple projects and priorities while ensuring timely delivery and measurable business outcomes. - Stay current with developments in AI/ML, GenAI, MLOps, and advanced analytics and evaluate their applicability to the organization. Preferred Candidate Profile: - 5 - 10 years of professional experience in Data Science, Machine Learning, Advanced Analytics, Applied AI, or a closely related field. - Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related quantitative discipline. - Strong hands-on expertise in Python and SQL. - Strong understanding of machine learning, statistical modeling, predictive analytics, and experimental design. - Proven experience developing and deploying machine learning models in production, rather than only building POCs. - Experience with frameworks such as Scikit-learn, XGBoost/LightGBM, TensorFlow, or PyTorch. - Experience working with large datasets using SQL, Pandas, PySpark, or similar technologies. - Good understanding of cloud platforms such as AWS, Azure, or GCP. - Exposure to MLOps, MLflow, Docker, CI/CD, model deployment, APIs, and production monitoring is highly desirable. - Experience with Generative AI/LLMs, NLP, RAG, embeddings, vector databases, or agentic AI would be an advantage, particularly for a modern AI-focused role. Current Indian postings increasingly include these capabilities. - Strong ownership mindset, attention to detail, and ability to work independently in a fast-paced environment. - Candidates with experience in product companies, startups, GCCs, consulting, BFSI, e-commerce, SaaS, or other data-intensive industries would be preferred, depending on the business domain.