Data Scientist

Role & responsibilities

We are looking for a Data Scientist with strong experience in data analytics, SQL, Databricks, and Data Analyst- With practical exposure to Generative AI and Agentic AI solutions.
The candidate will work closely with business stakeholders, Data Engineers, Data Scientists, and AI/technical teams to understand business requirements, analyze and validate data, and support data and AI solution delivery.

Key Responsibilities

Analyze business requirements and translate them into clear functional and data requirements.
Perform hands-on data analysis using SQL and Databricks, including data validation, profiling, and troubleshooting.
Work with Databricks notebooks, tables, queries, and analytical datasets.
Support data modeling and identify relevant data sources for analytics and AI use cases.
Work with data governance concepts such as Unity Catalog, metadata, lineage, permissions, and trusted datasets.
Collaborate with Data Scientists, Data Engineers, and AI Engineers on Data Science, GenAI, and Agentic AI use cases.
Understand and work with concepts such as LLMs, RAG, embeddings, vector search, prompt engineering, AI agents, tool/function calling, APIs, and agent workflows.
Understand how enterprise data is retrieved, validated, governed, and consumed by AI solutions.
Support UAT and solution validation for data, analytics, and AI solutions.
Validate AI outputs for accuracy, relevance, grounding, and potential data issues.
Identify data quality issues and help troubleshoot discrepancies in analytical or AI outputs.
Participate in Agile delivery activities including backlog refinement, sprint planning, requirements clarification, and defect management.
Work effectively with both business and technical stakeholders.

Preferred candidate profile

4-9 years of experience in Data Engineer and Data Scientist, Business/Data Analysis, or a related role.
Strong hands-on SQL and data analysis skills.
Experience working with Databricks or a similar modern data platform.
Good understanding of data modeling, data quality, validation, and data relationships.
Experience gathering and documenting business/functional requirements.
Understanding of Data Science concepts and experience working with or alongside Data Science teams.
Practical experience or strong working knowledge of Generative AI / LLM-based solutions.
Exposure to Agentic AI / AI Agent solutions and an understanding of how they work with enterprise data.
Understanding of RAG, embeddings, vector search, tool/function calling, APIs, and AI agent workflows.
Experience with UAT, Agile methodologies, and stakeholder communication.

Good to Have
Experience with Databricks and Azure.
Python & SQL experience.
Exposure to Databricks Mosaic AI or similar AI platforms.
Experience with MLflow or AI/LLM evaluation.
Knowledge of AI governance, observability, or guardrails.
Engineering domain knowledge would be a strong plus.
Experience in Engineering, marketing, and enterprise domains.
Ideal Candidate
The ideal candidate should be able to bridge the gap between:
Business Requirements Data & Analytics Data Science GenAI / Agentic AI Technical Delivery
We are looking for someone who is technically strong in data and analytics and has enough exposure to modern AI technologies to work effectively with Data Science and AI teams.