Sr. Data Scientist

Chubb•Jersey City, NJ

About The Position

Chubb’s North America Data Analytics team is seeking a Senior Data Scientist to develop AI and machine learning solutions for business partners. The role focuses on solving complex business problems by combining industry-standard practices with innovative approaches, and on building pipelines and designing architecture to support AI/ML capabilities. The position offers exposure to a wide variety of analytical tools and technologies. The Senior Data Scientist will collaborate with business partners and technical peers to understand problems, gather requirements, and facilitate data integration. The role includes developing, implementing, and testing AI/ML models on Azure; analyzing data and translating findings into business insights; researching and implementing predictive modeling approaches, including zero-shot and few-shot learning, embeddings, and fine-tuning; and integrating data and document sources through APIs or pipelines. The position also requires producing high-quality, secure code and communicating effectively with stakeholders across Chubb. The position is open to the Jersey City, Whitehouse Station, or Madison Avenue locations in New York City, New York, or Philadelphia, Pennsylvania. Limited travel to various work locations is expected for in-person collaboration and meetings. The pay range is $125,400 to $182,600, with the specific offer dependent on skills and other factors. The role may also be eligible for a discretionary annual incentive program. Chubb offers a comprehensive benefits package; further details are available on its careers website.

Requirements

At least 4 years of hands-on experience with supervised and unsupervised machine learning algorithms.
Deep expertise in writing and reviewing production code in Python/PySpark.
Knowledge of machine learning frameworks and libraries such as AutoML, TensorFlow, or PyTorch, and exposure to machine learning algorithms and their practical production implementation at large scale.
Experience designing and deploying scalable end-to-end machine learning/NLP systems as Databricks jobs or dockerized containers, and familiarity with API frameworks/tools such as FastAPI.
Experience with NLP techniques including text cleaning and preprocessing, entity extraction, encoder-decoder architectures, and similarity matching.
Working knowledge of or familiarity with Git version control.
Experience with prompt engineering, chunking, and LLM evaluation techniques.
Ability to multitask, learn quickly, solve problems, and communicate effectively.
Collaborative mindset, willingness to share knowledge, and interest in keeping up with industry trends.
Interest in learning about the latest generative AI technologies and processes.
Preferred bachelor's or master's degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Operations Research, Economics, or Engineering; an advanced degree is a plus.

Nice To Haves

Prior experience in property and casualty insurance and/or knowledge of claims and underwriting functions.
Hands-on experience with big data technologies and cloud platforms such as Databricks, Spark, Azure, AWS, GCP, or Snowflake.
Advanced knowledge of model tuning, evaluation, and operationalization.
Extensive understanding of LLM functionality and inference techniques.
Experience with deep learning libraries such as TensorFlow/Keras, PyTorch, or MXNet.
Comfort with command-line scripting in Linux or Windows.
Familiarity with generative AI techniques such as GANs, VAEs, or other generative models.
Experience architecting and consuming APIs in a scalable, multithreaded or batched fashion.
Experience with at least one additional programming language, such as R, Julia, Scala, Go, Java, or C++.
Exposure to DBT, Apache Airflow, or Luigi.
Working knowledge of containers, specifically Dockerizing APIs.
Exposure to CI/CD pipelines.
Comfort with at least one NoSQL database, such as MongoDB, Elasticsearch, or CosmosDB.

Responsibilities

Collaborate with business partners and organizational peers to understand and scope problems, gather business requirements, and facilitate data integration.
Develop, implement, and test artificial intelligence and machine learning models to address complex business challenges on the Azure platform, as an individual contributor and/or within a team.
Synthesize data to identify trends, assess the impact of data on business usage, and recommend improvements.
Perform data validation, preprocessing, and exploratory data analysis, and translate analytical findings into relevant business insights.
Research and implement predictive modeling approaches, including zero-shot and few-shot learning, embedding techniques, and fine-tuning.
Ensure code quality, alignment with business objectives and quality standards, and compliance with secure web development guidelines.
Integrate data and document sources using APIs and/or data pipelines.
Build effective working relationships with business partners across Chubb, IT, and Analytics peer groups.
Communicate effectively with stakeholders in writing, orally, and through presentations.