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.