Analyst, Gen AI, Data Science

About the Role

AIG is reimagining how we help customers manage risk. Join us as an Analyst in the Generative AI, Data Science department to play a part in that transformation. This is an opportunity to shape a newly formed team with resources and support to explore fresh, creative approaches.

Key Responsibilities

• Support the development and implementation of data science and generative AI models that deliver real-world impact, including hands-on data engineering.
• Collaborate with teams across the business—product managers, business leaders, engineers, and others—to create data-driven solutions.
• Develop end-to-end data science & AI/ML projects, from conceptualization to operational rollout, ensuring solutions align with ethical and regulatory standards.
• Foster 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 performance.
• Drive continuous improvement of AI capabilities by staying at the forefront of emerging technologies and identifying how they can enhance operations.
• Contribute to day-to-day direction of multiple components of Data Science activities.

Requirements & Tech Stack

• Python proficiency for data wrangling, machine learning, and deep learning using packages such as pandas, scikit-learn, TensorFlow, torch, transformers, LangChain, etc.
• Data engineering skills in Python and PySpark.
• Experience and knowledge of using Generative AI models, with an understanding of deep learning model classes such as GPT, VAE, and GANs, as well as their hyperparameters.
• Hands-on desire to use platforms like Palantir Foundry, Snowflake, and AWS SageMaker.

Qualifications

• Master’s degree to be received no later than June 2027 in a STEM major, primarily Data Science, Computer Science, or Math.
• Available to start the Program on July 26, 2027.
• Strong written and verbal communication skills.
• Demonstrated analytical, problem-solving, and organizational skills, with strong attention to detail.
• Ability to collaborate effectively, manage multiple priorities, and adapt in a fast-paced, team-oriented environment.
• Coursework in STEM (Math, Physics, Statistics, Computer or Data Science).
• Pursuing advanced degrees, Masters or PhD is a strong plus.
• Knowledge of applied data science in a product development environment.
• Experience across the full ML lifecycle.
• Experience building or studying frameworks to measure LLMs efficacy, ground truth dataset quality, and guide product development roadmap.
• Experience or a strong willingness to learn data science techniques such as Gen AI, deep learning, machine learning, statistical modelling, NLP, NLU.