Manager - Data Science

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

  .