Data Scientist / Sr Data Scientist

  • Chubb
  • Chicago, IL, United States

Job Description

Data Scientist / Senior Data Scientist

Work Locations: London – Leadenhall, US – Chicago, WFH

Job Field: Information Technology – Data Scientist / Senior Data Scientist

Employment Type: Permanent employee


The Opportunity

Overall, supports the Business Intelligence (BI) Lead, and be responsible for developing and deploying a BI solution with key analytics in order to add value to the organization. The role will work alongside a team of highly capable and technically proficient developers in order to produce insightful business analytic applications. Projects may involve optimization / redesign of existing processes and documents, as well as a ground-up approach on any new initiatives.


About Chubb

Chubb is the world’s largest publicly traded property and casualty insurer. With operations in 54 countries, 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.

The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.

The insurance companies of Chubb serve multinational corporations, mid-size and small businesses with property and casualty insurance and services; affluent and high net worth individuals with substantial assets to protect; individuals purchasing life, personal accident, supplemental health, homeowners, automobile and other specialty insurance coverage; companies and affinity groups providing or offering accident and health insurance programs and life insurance to their employees or members; and insurers managing exposures with reinsurance coverage.







Job Requirements:

Skills and Requirements

  • Required:
    • Strong academic qualifications (MSc. or studying towards MSc/PhD.) in relevant subject areas (Computer Science, Machine Learning, Statistics, Business Intelligence, Applied Mathematics, Physics, Big Data etc.)
    • Excellent understanding of descriptive, inferential, Bayesian statistics, probability theory, machine learning/predictive analytics and data mining.
    • 2+ years relevant quantitative and qualitative research and analytics experience.
    • Experience in the Insurance industry is a plus
    • Software development skills, working with distributed data sets (Apache Spark), cloud ML development (AWS, Azure, Databricks, Dataiku etc.), time-series forecasting, NLP.
    • Experience with data visualization tools, preferably within Python/R and/or Qlik a plus.
    • Ability to cleanse and transform data into useable forms using SQL, Python and/or R.
    • Strong experience with the Microsoft suite of products
    • Ability to check, debug and problem solve issues to ensure you deliver accurate and clear analysis reports, quickly, even when confronted by subtle data complications.
    • Ability to adapt to rapidly and constantly changing stakeholder requirements.
    • Quick to learn, ability to prioritise activities and responsive to the needs of the business.
    • Be able to communicate and present on complex technical concepts to specialist and non-specialist audiences.
  • Desirable
    • Deep learning (CNN, RNN, LSTM) and framework library (e.g., Keras, TensorFlow, PyTorch)
    • Machine learning lifecycle management including feature engineering, model building, model versioning, evaluation and deployment.
    • Working knowledge of Git for code version control


About You

  • You’ll be an excellent team player, with a flexible attitude.
  • Data curiosity to understand problems and surface interesting facts
  • An analytical / data-driven approach.
  • Be able to work independently and manage your time effectively.
  • Have strong problem-solving skills.
  • Excellent organisational skills with the ability to manage multiple priorities.
  • Strong communication skills with both written and verbal content.
  • Focus and dedication to culture.
  • Working to deadlines.

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