Principal Data Scientist

Role & responsibilities

• Lead the design, development, and deployment of advanced machine learning, AI, and statistical models to solve complex business problems.
• Define the data science strategy, technical roadmap, modeling approach, and analytical direction for high-impact initiatives.
• Identify business opportunities where AI/ML, predictive analytics, Generative AI, and advanced analytics can create measurable value.
• Develop and productionize scalable solutions across machine learning, deep learning, NLP, computer vision, recommendation systems, forecasting, and optimization.
• Lead end-to-end data science projects from problem formulation and data exploration to model development, validation, deployment, and monitoring.
• Apply advanced statistical methods, experimentation, hypothesis testing, and causal analysis to generate actionable insights.
• Work closely with Product, Engineering, Data Engineering, Business, and senior leadership teams to translate business requirements into scalable AI/ML solutions.
• Drive model performance, scalability, reliability, explainability, and responsible AI practices.
• Mentor and provide technical leadership to Data Scientists, ML Engineers, and Analytics teams.
• Establish best practices for feature engineering, model selection, experimentation, MLOps, model governance, and deployment.
• Evaluate emerging technologies, research papers, frameworks, and AI/ML techniques and assess their applicability to business problems.
• Lead initiatives involving Generative AI, LLMs, NLP, RAG, embeddings, fine-tuning, and LLMOps, where applicable.
• Partner with engineering teams to build robust data pipelines, ML platforms, APIs, and production-grade model infrastructure.
• Communicate complex analytical findings, model results, risks, and recommendations to technical and non-technical stakeholders.
• Define and track business and model KPIs to demonstrate measurable impact from data science initiatives.

Preferred candidate profile

• 5 to 10 years of experience in Data Science, Machine Learning, Artificial Intelligence, Advanced Analytics, or a related field; principal-level candidates should demonstrate significant technical leadership.
• Strong expertise in Machine Learning, Statistical Modeling, Predictive Analytics, Deep Learning, and Data Science.
• Advanced proficiency in Python and SQL; experience with R, PySpark, or Scala is an added advantage.
• Strong hands-on experience with Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent frameworks.
• Strong understanding of statistics, probability, optimization, hypothesis testing, experimentation, and mathematical modeling.
• Experience building and deploying production-grade ML models and scalable AI solutions.
• Strong knowledge of MLOps, MLflow, model deployment, model monitoring, CI/CD, Docker, Kubernetes, and ML lifecycle management.
• Experience with cloud platforms such as AWS, Azure, or GCP and modern data platforms such as Databricks, Snowflake, or Spark.
• Experience with Generative AI, LLMs, NLP, RAG, embeddings, prompt engineering, fine-tuning, or LLMOps is highly desirable.
• Experience working with large datasets, distributed computing, data pipelines, ETL, feature engineering, and big data technologies.
• Proven ability to lead complex projects and influence technical decisions across Data Science, Engineering, Product, and Business teams.
• Strong technical leadership, mentoring, stakeholder management, communication, and problem-solving skills.
• Ability to translate complex business problems into data-driven, scalable, and measurable solutions.
• Strong research mindset with the ability to evaluate new algorithms, methodologies, tools, and industry trends.
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related quantitative discipline.
• M.Tech/M.E./M.Sc./MCA or Ph.D. in a relevant field is an added advantage, particularly for research-intensive roles.