Data Scientist

Responsibilities

• Monitor and evaluate the outputs of production Agentic AI models, verifying performance metrics and maintaining agent evaluation data.
• Design, test, and optimize prompt engineering strategies to improve model response accuracy, latency, and relevance.
• Perform exploratory data analysis and build transformation pipelines using Python and SQL to support ongoing model performance.
• Support API frameworks such as FastAPI or Flask that serve model predictions and agent decisions to upstream finance systems.
• Collaborate with MLOps and Run Lead to troubleshoot model drift, logic errors, or connection issues to underlying databases.
• Develop an understanding of complex financial business processes (such as SAP, Salesforce, and Postgres tables) to ensure model logic matches business requirements.
• Produce and analyze insights from key performance indicators of models, proposing updates and modifications where necessary.
• The role requires being hands-on and working together with external and internal partners to develop, test and operationalize data science and analytical solutions as well as ensure its adoption by businesses.
• Work to leverage data (Transactional / Big Data, External/ Internal Data, Structured/ Unstructured Data) and analytics methods to develop analytics solutions
• Qualifications
• 6 - 8 years of experience preferred
• BTech-BE from Tier 1/2 engineering schools or MStat/MS (Math)/MA Econ/ MS Analytics/data science from Tier 1/2 schools
• Direct experience with Prompt Engineering, Agent Engines, Agent Evaluation, and Agent Observation frameworks.
• Experience with GCP Cloud Platform, including GCP Cloud Functions and BigQuery.