Lead Applied Data Scientist – GenAI

Lead Applied Data Scientist – GenAI

Requirements

• significant experience as an applied data scientist and/or data engineer
• recent hands-on experience with Retrieval-Augmented Generation (RAG), prompting and model fine-tuning
• experience across the full ML-Ops lifecycle
• deep expertise in data science techniques such as deep learning, machine learning, statistical modelling, NLP
• strong data engineering skills in Python and PySpark
• proficient with one or more modern AI/ML platforms (e.g. Palantir Foundry, AWS SageMaker, Azure ML or Snowflake)
• experienced with cloud-based cognitive services
• strong experience using key Python packages for data wrangling, machine learning and deep learning
• experience using GitHub code version

Responsibilities

• Own the end-to-end development and deployment of data science and GenAI/ML models from conceptualization to operational rollout, while ensuring that all solutions align with ethical and regulatory standards
• Collaborating with teams across the business—product managers, business leaders, engineers, and others—to create data-driven solutions that make a difference
• Fostering a collaborative and innovative environment where knowledge-sharing and creativity thrive among the data science community
• Utilize Retrieval-Augmented Generation (RAG) and Prompt Engineering and few-shot techniques to enhance LLM’s performance on specific tasks
• Build evaluation frameworks to measure LLMs efficacy, ground truth dataset quality, and guide product development roadmap
• Driving continuous improvement of AI capabilities by staying at the forefront of emerging technologies and identifying how they can enhance our operations
• Contributing to day-to-day direction of multiple components of Data Science activities

Preferred Qualifications

• ideally applied experience in financial services or insurance