Generative AI Solutions Architect

2 days ago


Atlanta, Georgia, United States BCG X Full time

**Transforming Insurance with Generative AI**

We are at the forefront of innovation in the insurance industry, leveraging cutting-edge technologies like Generative AI to drive business growth and transformation.

**Job Title:** Lead Applied Data Scientist - Insurance Client

**Estimated Salary:** $170,000 - $220,000 per year

About the Role:

This is an exciting opportunity to join our client's innovative team, working on groundbreaking Generative AI initiatives that will shape the future of the insurance industry. As a Lead Applied Data Scientist, you will support the development and delivery of AI solutions that drive business growth and transformation.

Your Key Responsibilities:

AI Solution Development: Develop and deploy innovative AI solutions that align with business objectives.Collaboration: Collaborate with cross-functional teams to create data-driven solutions that make a tangible impact.Team Enablement: Foster a culture of collaboration and knowledge-sharing among data science professionals.Technical Expertise: Leverage Retrieval-Augmented Generation (RAG) and Prompt Engineering techniques to enhance LLM performance on specific tasks.Evaluation Frameworks: Design and implement evaluation frameworks to measure LLM efficacy and inform product development roadmap.Emerging Technologies: Stay ahead of the curve with emerging technologies and identify opportunities to enhance operations.Direction of Data Science Activities: Contribute to the daily direction of multiple components of Data Science activities.

Requirements:

You have significant experience as an applied data scientist and/or data engineer, with recent hands-on experience with Retrieval-Augmented Generation (RAG), prompting, and model fine-tuning. Experience across the full ML-Ops lifecycle is crucial. Ideally, you have experience in financial services or insurance, and deep expertise in data science techniques such as deep learning, machine learning, statistical modeling, and NLP.

Necessary Tools and Experience:

You possess strong data engineering skills in Python and PySpark, proficiency with one or more modern AI/ML platforms, and experience with cloud-based cognitive services. Strong experience using key Python packages for data wrangling, machine learning, and deep learning is also required. Experience with GitHub code versioning is expected.



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