In this position, you will work under the guidance of more senior data science staff on modeling projects but is expected to ensure the appropriate modeling and analytic methodologies are applied within the scope of a given project.
In addition, you will work on small scale or ad hoc data science projects independently.
Work closely with data engineering and infrastructure teams to deploy models and data products at scale.
Education and Experience:
- Bachelor's degree in Statistic, Computer Science, Engineering, Mathematics or related field is required
- Master's degree in a qualitative field is preferred
- 2+ years' experience predictive analytics, data mining or statistical analysis in the insurance industry or 4+ years predictive analytics, data mining or statistical analysis in other industry
- Experience with Git or similar tool for version control
- Experience developing or applying generative AI models (such as large language models or generative adversarial networks) to solve business problems is a plus
- Proficiency with natural language processing (NLP) techniques and tools for extracting insights from unstructured data, including experience with prompt engineering or deploying AI-powered chatbots and virtual assistants is a plus
- Experience working with big-data technology on Linux-based systems is a plus
- Experience with deep learning frameworks (TensorFlow, Keras, PyTorch, etc.) is a plus
- Experience with Bayesian programming languages/frameworks such as Stan, or PyMC3 is a plus
Knowledge and Skills:
- Hands-on experience in Python, R, SQL, Scala
- Exposure to cloud computing (Azure, AWS, etc.)
- Solid foundation in statistics and machine learning models, processes, and theories, with the ability to evaluate different algorithmic approaches
- Ability to write production-ready code
This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Other duties, responsibilities and activities may change or be assigned at any time with or without notice.
