Jobs · Engineering · Connecticut

Data Scientist

Munich Re Automation Solutions · Hartford, CT · 1 wk ago
EngineeringFull-time

About the role

In this position, you will work under the guidance of more senior data science staff on modeling projects but are expected to ensure the appropriate modeling and analytic methodologies are applied within the scope of a given project. You will also work on small-scale or ad-hoc data science projects independently. Additionally, you will collaborate closely with data engineering and infrastructure teams to deploy models and data products at scale.

Responsibilities

  • Apply appropriate modeling and analytic methodologies to projects under guidance from senior staff.
  • Execute small-scale or ad-hoc data science projects independently.
  • Work with data engineering and infrastructure teams to deploy models and data products at scale.

Requirements

  • Bachelor’s degree in Statistics, Computer Science, Engineering, Mathematics, or a related field (required).
  • Master’s degree in a quantitative field (preferred).
  • 2+ years of experience in predictive analytics, data mining, or statistical analysis in the insurance industry, or 4+ years in another industry.
  • Experience with Git or a similar version control tool.

Qualifications

  • Hands-on experience with Python, R, SQL, or 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.

Skills (Preferred)

  • Experience developing or applying generative AI models (e.g., large language models or generative adversarial networks) to solve business problems.
  • Proficiency with natural language processing (NLP) techniques and tools for extracting insights from unstructured data, including prompt engineering or deploying AI-powered chatbots and virtual assistants.
  • Experience working with big-data technology on Linux-based systems.
  • Experience with deep learning frameworks (TensorFlow, Keras, PyTorch, etc.).
  • Experience with Bayesian programming languages/frameworks such as Stan or PyMC3.

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