Data Scientist - Senior Lead Engineer - IT

You are passionate about cutting-edge technology and keen on applying GenAI algorithms and approaches. You are analytically driven, intellectually curious, and experienced leading the development and implementation of data and analytic solutions to solve challenging business problems. You are mostly an Individual Contributor who enjoys cracking hard to solve problems with recent LLMs and seeing it deployed in-market and generating value for Guardian. You enjoy collaborating with a multi-disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders. You have demonstrated track record of managing data science teams and working with business leaders. 

You have:

• PhD with 6+ years of experience, Master's degree with 8+ years of experience, or Bachelor's degree with 10+ years of experience in Computer Science, Engineering, Applied mathematics or related field
• 7+ years of hands-on ML modeling/development experience 
• Experience in creating GenAI and Agentic AI applications 
• Solid working knowledge of the engineering components essential in a Gen AI application, including Vector DB, caching layer, chunking, and embedding
• Knowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Demonstrated track records in experimental design and executions
• Strong skills in Python, PyTorch, Langchain, LangGraph etc
• Solid background in algorithms and a range of ML models
• Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level
• Excellent analytical and problem-solving abilities with superb attention to detail
• Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success