Data Scientist

Overview

Build AI workflows that extract information from unstructured user inputs and data to make recommendations Design and implement multi-turn conversational capabilities to handle clarifications, context retention, and error recovery Integrate AI models with enterprise systems (HR portals, Webex) to enable various use cases Monitor model performance, analyze data trends, and continuously improve model metrics and user experience.

Ensure data privacy, security, and ethical handling of sensitive and PII information. Collaborate with engineering and product teams to align AI capabilities with business goals and user needs.

Generate reports and insights on application patterns, agent effectiveness, and model improvements.

Prototype and experiment with new GenAI techniques, staying abreast of advancements in the field to maintain a cutting-edge approach

Qualification

Mandatory Reasonably good knowledge of modern LLM and Agentic architecture.

Demonstrated experience working with Generative AI, LLMs (e.g., GPT, Claude), and prompt engineering for real world solutions

Ability to write code to deploy agentic frameworks and quantitatively analyze output quality.

Working proficiency and knowledge of NLP in general use NLP frameworks from python independently and understand model semantics and quality.

Working knowledge of API integration and enterprise grade quality checks for connecting to frontends such as Webex and MS Teams.

Good idea of model observability and feedback collection from dashboards/logs and using feedback and usage data systematically to improve agent performance.

5+ years of proven experience in a Data Scientist role, ideally in dynamic or innovative environments

Bachelors or masters degree in computer science, Data Science, Statistics, Mathematics, or a related field

Preferred Qualification

Deep expertise in machine learning, statistical modeling, and experimental design Track record of rapid prototyping and working closely with cross-functional teams Apply advanced statistical analysis and machine learning methodologies to model, interpret, and extract insights from complex data Ability to operate in ambiguity, self-motivate, and learn rapidly in a fast-paced environment