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.
