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…