
Staff Data Scientist
- Bangalore, Karnataka
- Permanent
- Full-time
- Design and implement end-to-end GenAI and Agentic AI solutions, leveraging AWS Bedrock and Databricks. This includes building Retrieval Augmented Generation (RAG) pipelines and multi-agentic systems to solve
- Build and optimize generative AI models, with a focus on fine-tuning and prompt engineering, using frameworks like LangChain and LangGraph. Implement regularization and architectural design patterns to ensure model reliability and prevent overfitting
- Collaborate with ML engineers to deploy models, establish CI/CD pipelines for GenAI, and implement robust model tracking and observability
- Implement systems to detect and address model drift, hallucinations, and safety guardrail failures, ensuring a high level of operational integrity
- Develop and plan our roadmap for our domain analytics and data engineering & science team
- Run scrum ceremonies with our Product/Business team
- Triage requests, create the work breakdown structure and assign it to respective Engineers/Scientists
- Work with Engineers, Scientists and governance team to identify challenges they face and work with them to identify solutions to these problems
- Ensure stakeholders are updated and informed about changes in our domain specific data needs
- Build and track metrics for the performance of our Engineering & Science team. Feedback to Product and Business Teams
- Ability to deal with ambiguity and propose innovative solutions without getting blocked
- You have 10+ years of experience in Software development or Machine Learning. With 4+ years of product management experience and at least 2 years as a Product Owner embedding data initiatives into products especially Data Science and Machine Learning
- Hands-on experience with LangGraph or similar frameworks for building reusable and scalable multi-agentic workflows
- Deep understanding of prompt engineering, RAG pipeline design (including chunking, embedding strategies, and vector store optimization), and fine-tuning techniques for LLMs
- Hands-on experience with both quantitative and qualitative evaluation metrics for GenAI, including benchmarks for RAG pipelines and methods for assessing the reliability and performance of agentic systems
- You can prioritise ML Data Science and Machine Learning product roadmaps for the respective businesses based on OKRs and priorities
- You have a deep understanding of managing technical products with a background in data
- You have a high level understanding with big-data technologies such as Spark, SparkML, Hadoop etc. Strong knowledge of Cloud (AWS or other)
- You've delivered on fast-growing product-focused company before as a Data Manager or Data Lead or Data Program manager (products where the customer is retail or small business - as opposed to internal-facing tools)
- You're organised, pragmatic and capable of engaging, guiding and leading cross functional teams or managing large scale enterprise products.
- You have technical knowledge and experience and have strong empathy for developer audience
- You're a self-starter who can work comfortably in a fast-moving company where priorities can change and processes may need to be created from scratch with minimal guidance.
- You have significant experience working with varied stakeholders
- You have good technical knowledge in SQL, strong in Python programming
- You have a good understanding on how the performance optimization works in the end to end data pipeline including ML/DS inferencing
- You have excellent leadership skills - you have managed a team of data scientists before and coached them to become better versions of themselves
- Databricks on AWS
- Python
- Snowflake
- Tecton - feature store
- Fiddler - model observability platform
- Competitive salary
- Self & Family Health Insurance
- Term & Life Insurance
- OPD Benefits
- Mental wellbeing through Plumm
- Learning & Development Budget
- WFH Setup allowance
- 15 days of Privilege leaves
- 12 days of Casual leaves
- 12 days of Sick leaves
- 3 paid days off for volunteering or L&D activities
- Stock Options