The North America Data Analytics team at Chubb is seeking a Sr. Data Scientist with 5+ years of industry experience to join our fast-paced, high-energy team. This team is responsible for delivering AI solutions to our business partners that will meet business objectives and move-the-needle to improve upon key performance metrics.
As a Sr. Data Scientist for North America Data Analytics focusing on AI and Machine Learning capabilities, you will develop solutions to complex business problems which combine industry standard practices with innovation. You will focus on building pipelines and designing architecture to facilitate AI/ML capabilities. This position offers exposure to a wide variety of analytic tools and technologies. Be ready to meet analytic challenges head-on as you develop and refine AI/ML data processes for Chubb.
In this role you will:
- Collaborate with business partners and peers within the organization to understand and scope the problem, gather business requirements, and facilitate the data integration process.
- Work as an individual contributor and/or as a team of data scientists to develop, implement and test Artificial Intelligence and Machine learning models to solve complex business challenges on Azure platform.
- Synthesize data to uncover inherent trends, assess impact of data on business usage, and to make recommendations for improvement.
- Perform data validation, preprocessing, and exploratory data analyses to translate analytical findings into relevant business insights
- Research & implement novel AI/ML predictive modeling approaches including but not limited to zero-shot/few-shot learning, embedding techniques, fine-tuning etc.
- Ensure high quality code that meets business objectives, quality standards and secure web development guidelines.
- Integrate with data / document sources using APIs and / or data pipelines.
- Create excellent working relationships with business partners across the Chubb organization, IT and Analytics peer groups.
- Effectively communicate with key stakeholders in written, oral and presentation formats.
Required:
- 4+ years’ hands on experience with both supervised and unsupervised Machine Learning (ML) algorithms, with deep expertise in writing, and reviewing production code in Python/PySpark.
- Knowledge of ML frameworks and libraries (such as AutoML, TensorFlow/Pytorch), and exposure to various ML algorithms and their practical implementation in production at large scale.
- Experience with designing and deploying scalable end-to-end Machine Learning/NLP systems as Databricks jobs/ dockerized containers and familiarity with API frameworks/tools such as FastAPI etc.,
- Experience with NLP techniques around text cleaning/pre-processing, entity extraction, encoder-decoder architectures, similarity matching etc.
- Working knowledge/familiarity with Git version control
- Experience with Prompt Engineering, Chunking, LLM evaluation techniques
- Ability to multi-task, learn new things quickly, and have excellent problem solving & communication skills.
- A mindset for collaboration, knowledge sharing and keeping up to date on emerging industry trends.
Appetite for learning and keeping up to date on the latest generative AI technologies and processes
Preferred:
- Prior experience in property and casualty insurance, and / or knowledge of claims and underwriting functions
- Hands-on experience with big data technologies and cloud (Databricks / Spark, Azure/AWS/GCP/Snowflake).
- Advanced knowledge of model tuning, evaluation, and operationalization
- Extensive understanding of LLM functionality and inference techniques
- Experience with Deep Learning libraries (Tensorflow/Keras, PyTorch, MXNet, etc.).
- Comfortable with command line (Linux, Windows) scripting.
- Familiarity with generative AI techniques, such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or other generative models.
Experience with architecting and consuming APIs in a scalable (multi-threaded/batched) fashion.
Nice to have:
- Experience with at least one other programming language (R, Julia, Scala, Go, Java, C++).
- Exposure to technologies such as DBT, Apache AirFlow or Luigi.
- Working knowledge of containers, specifically Dockerizing APIs.
- Exposure to CI-CD pipelines.
- Comfortable with at least one NoSQL database (MongoDB, ElasticSearch, CosmosDB, etc.).
Education
Bachelor’s / Master’s degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Operations Research, Economics or Engineering are preferred (advanced degree a plus).
Location:
We are open to the following work locations for this position: (Jersey City, NJ, Whitehouse Station, NJ, Madison Avenue, NYC, or Philadelphia, PA).
Please note there will be limited expected travel to various work locations for in person collaboration and meetings.
The pay range for the role is $125,400 to $182,600. The specific offer will depend on an applicant’s skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program. Chubb offers a comprehensive benefits package, more details on which can be found on our careers website. The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.
About UsSkills Required
- 4+ years of hands-on experience with supervised and unsupervised machine learning algorithms
- Deep expertise writing and reviewing production code in Python and PySpark
- Knowledge of machine learning frameworks and libraries such as AutoML, TensorFlow, or PyTorch
- Experience deploying scalable end-to-end machine learning and NLP systems using Databricks jobs or Dockerized containers
- Familiarity with API frameworks and tools such as FastAPI
- Experience with NLP techniques including text preprocessing, entity extraction, encoder-decoder architectures, and similarity matching
- Working knowledge of Git version control
- Experience with prompt engineering, chunking, and large language model evaluation techniques
- Strong multitasking, problem-solving, learning, communication, collaboration, and knowledge-sharing abilities
- Experience in property and casualty insurance 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 large language model functionality and inference techniques
- Experience with deep learning libraries such as TensorFlow, Keras, PyTorch, or MXNet
- Comfort with Linux or Windows command-line scripting
- Familiarity with generative AI techniques such as GANs, VAEs, or other generative models
- Experience architecting and consuming APIs in scalable, multithreaded, or batched environments
- 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 Dockerized APIs and containers
- Exposure to CI/CD pipelines
- Comfort with at least one NoSQL database such as MongoDB, Elasticsearch, or Cosmos DB
- 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
Chubb Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Chubb and has not been reviewed or approved by Chubb.
-
Healthcare Strength — Healthcare coverage includes multiple medical plan options with preventive care, plus dental and vision, alongside HSAs/FSAs, telemedicine, and wellness resources. The breadth of offerings is presented as comprehensive for a large insurance employer.
-
Retirement Support — A 401(k) plan with company contributions anchors the financial benefits, complemented by life and disability insurance. These programs reinforce long-term savings and protection.
-
Strong & Reliable Incentives — Performance-based and year-end bonuses are part of the total rewards, with structured reviews supporting incentive opportunities. These components provide compensation beyond base salary.
Chubb Insights
What We Do
Chubb is the world’s largest publicly traded property and casualty insurance company. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. As an underwriting company, we assess, assume and manage risk with insight and discipline. We service and pay our claims fairly and promptly. The company is also defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally. Parent company Chubb Limited is listed on the New York Stock Exchange (NYSE: CB) and is a component of the S&P 500 index. Chubb maintains executive offices in Zurich, New York, London, Paris and other locations, and employs 31,000 people worldwide. Additional information can be found at: chubb.com.
