Hands-on AI & ML Development:

Build, train, and deploy ML models from classical techniques (Classification, Regression, Forecasting) to advanced deep learning, LLMs, and Generative AI.
Perform feature engineering, statistical analysis, and model tuning to enhance predictive capabilities.
Work with big data tools (Hadoop, PySpark, Hive) to process large datasets efficiently.
Develop AI-driven solutions tailored for industries like Retail, CPG, BFSI, Healthcare, and eCommerce.
Optimize and automate machine learning pipelines for scalable deployment.

End-to-End AI/ML Deployment:

Own the full ML lifecycle, from development to deployment and monitoring.
Collaborate with Data Engineering teams to build production-ready ML pipelines.
Work with cloud platforms such as Azure, AWS, GCP, or Databricks for model deployment and monitoring.
Design and implement A/B testing strategies to measure model impact.

Project Execution & Delivery:

Drive multiple AI/ML initiatives from concept to execution, ensuring quality and timely delivery.
Partner with cross-functional teams (Product, Data Engineering, Business) to translate business problems into scalable AI solutions.
Own project roadmaps, track milestones, and ensure stakeholder alignment.
Communicate AI/ML concepts effectively to both technical and non-technical stakeholders.

Leadership & Team Growth:

Lead by doing mentor and guide a high-performance team of data scientists.
Set technical direction, review code, and establish best practices in AI/ML development.
Foster a culture of continuous learning, innovation, and hands-on problem-solving.
Provide career development guidance and help team members achieve technical excellence.

What You Bring to the Table:

8+ years of hands-on experience in Data Science & AI, with strong expertise in ML, Python, and SQL.
Exposure to Generative AI Should have worked on at least a few POCs or pilot projects leveraging Gen AI capabilities
Deep understanding of ML algorithms, including NLP, LLMs, forecasting, and optimization techniques.
Proven track record of building and deploying machine learning models in production environments.
Expertise in at least one cloud platform: AWS, Azure, GCP, or Databricks.
Strong grasp of statistics, probability, and causal inference.
Ability to break down complex AI concepts for business stakeholders.
Experience with big data tools (Hadoop, Hive, PySpark) and ML workflow automation.
Bonus: Experience with Google Analytics, Adobe Analytics, or digital marketing analytics.

Manager - Data Science