Data Science-II

Key Responsibilities
Leverage advanced data science and analytical skills to extract insights from complex datasets. Design, develop, implement, evaluate, and validate machine learning and AI solutions. Develop and support end-to-end Retrieval-Augmented Generation (RAG) implementations, including evaluation, validation, and proofing. Work directly with Large Language Models (LLMs), Generative AI, and Transformer-based architectures. Apply agentic AI approaches and MCP capabilities to enterprise and customer-facing product use cases.

Work with unstructured data to develop AI and data science solutions. Collaborate with Machine Learning Engineers throughout the model and product production lifecycle. Apply machine learning algorithms including deep learning, gradient boosting, and random forests. Work with large language models and Transformer-based architectures such as BERT, RoBERTa, and T5. Apply LLM technologies and platforms including ChatGPT, GPT-3.5, Claude, Mistral, and similar models. Work with large datasets and distributed computing technologies such as Hadoop and Spark.

Develop high-quality Python code for data science, machine learning, and AI applications. Translate analytical and modeling outcomes into actionable insights and innovative solutions. Collaborate with cross-functional teams to enhance business strategies and support data-driven decision-making. Contribute to customer-facing product development and ensure data science solutions meet product and business requirements. Mentor, train, and serve as a subject matter expert to guide colleagues and support team development.

Communicate technical concepts, analytical findings, model behavior, and recommendations clearly to technical and non-technical stakeholders.

Required Qualifications
10+ years of experience
Bachelor's Degree

Skills
Python Programming
Large Language Models (LLMs)
Transformer-based Architectures
Deep Learning
Gradient Boosting
Random Forests
Hadoop
Spark
Retrieval-Augmented Generation (RAG) Implementation
Generative AI Application
Agentic AI Application
Model Context Protocol (MCP) Application
Unstructured Data Processing
Data Science Solution Development for Customer-Facing Products
Collaboration with Machine Learning Engineers
Analytical Problem Solving
Cross-functional Team Collaboration
Subject Matter Expertise
Mentoring and Coaching
Technical Communication
Stakeholder Communication
Enterprise Data Science and AI Solutions Delivery

Schedule
Start date: 2026-09-10