Insurance Data Science Leader
5 days ago
BCG X is seeking a seasoned Lead Applied Data Scientist to join our team in shaping the future of insurance. With a focus on Generative AI, you will play a pivotal role in driving innovation and creating meaningful impact at scale.
About the RoleWe are looking for an exceptional data science leader to oversee the development and deployment of cutting-edge AI solutions. As a Lead Applied Data Scientist, you will collaborate with cross-functional teams to create data-driven solutions that transform the insurance industry.
Your Responsibilities- Develop and deploy end-to-end data science and GenAI/ML models from conceptualization to operational rollout, ensuring alignment with ethical and regulatory standards.
- Collaborate with product managers, business leaders, engineers, and other stakeholders to create impactful data-driven solutions.
- Foster a collaborative and innovative environment where knowledge-sharing and creativity thrive among the data science community.
- Apply Retrieval-Augmented Generation (RAG) and Prompt Engineering techniques to enhance Large Language Model (LLM) performance on specific tasks.
- Build evaluation frameworks to measure LLM efficacy, ground truth dataset quality, and guide product development roadmaps.
- Drive continuous improvement of AI capabilities by staying at the forefront of emerging technologies and identifying opportunities to enhance operations.
- Contribute to day-to-day direction of multiple components of Data Science activities.
- 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.
- Ideal experience in financial services or insurance.
- Deep expertise in data science techniques such as deep learning, machine learning, statistical modeling, and NLP.
The ideal candidate will have strong data engineering skills in Python and PySpark, proficiency with modern AI/ML platforms (e.g., Palantir Foundry, AWS SageMaker, Azure ML or Snowflake), and experience using key Python packages for data wrangling, machine learning, and deep learning. Experience with GitHub code version control is also essential.
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