Data Scientist

Key Responsibilities

• Build Gen AI-enabled solutions using online and offline LLMs, SLMs and TLMs tailored to

domain-specific problems.

• Deploy agentic AI workflows and use cases using frameworks like LangGraph, Crew AI etc.

• Apply NLP, predictive modelling and optimization techniques to develop scalable machine

learning solutions.

• Integrate enterprise knowledge bases using Vector Databases and Retrieval Augmented

Generation (RAG).

• Apply advanced analytics to address complex challenges in Healthcare, BFSI and

Manufacturing domains.

• Deliver embedded analytics within business systems to drive real-time operational insights.

Required Skills & Experience

• 3–5 years of experience in applied Data Science or AI roles.

• Experience working in any one of the following domains: BFSI, Healthcare/Health Sciences,

Manufacturing or Utilities.

• Proficiency in Python, with hands-on experience in libraries such as scikit-learn, TensorFlow

• Practical experience with Gen AI (LLMs, RAG, vector databases), NLP and building scalable

ML solutions.

• Experience with time series forecasting, A/B testing, Bayesian methods and hypothesis

testing.

• Strong skills in working with structured and unstructured data, including advanced feature

engineering.

• Familiarity with analytics maturity models and the development of Analytics Centre of

Excellence (CoE’s).

• Exposure to cloud-based ML platforms like Azure ML, AWS SageMaker or Google Vertex AI.

• Data visualization using Matplotlib, Seaborn, Plotly; experience with Power BI is a plus.

What We Look for (Values & Behaviours)

• AI-First Thinking – Passion for leveraging AI to solve business problems.

• Data-Driven Mindset – Ability to extract meaningful insights from complex data.

• Collaboration & Agility – Comfortable working in cross-functional teams with a fast-paced

mindset.

• Problem-Solving – Think beyond the obvious to unlock AI-driven opportunities.

• Business Impact – Focus on measurable outcomes and real-world adoption of AI.

• Continuous Learning – Stay updated with the latest AI trends, research and best practices.