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Lead Operational Research Data Scientist

Lead Operational Research Data Scientist

CHUBBMontreal, QC, Canada
Il y a plus de 30 jours
Salaire
30,00 $CA –40,00 $CA par heure
Description de poste

Chubb is a world leader in insurance. 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.

Key Objective :

Chubb is seeking a highly skilled and experienced Operational Research Data Scientist to join our team. This critical role will be responsible for leading the optimization efforts for a new initiative, leveraging advanced OR techniques and AI technologies to solve core insurance problems dynamically. The ideal candidate will have a proven track record of delivering impactful optimization solutions and be an expert in the application of mathematical models to solve complex business problems.

Major Responsibilities :

  • Develop and implement optimization models to improve business performance and operational efficiencies in the insurance industry
  • Analyze large volumes of data and provide insights to make data-driven decisions for use in insurance risk profiling, claims management and other key problem areas
  • Collaborate with cross-functional teams including actuaries, underwriters, and IT to design, develop, and integrate optimization solutions
  • Lead the end-to-end data science lifecycle from problem definition to model implementation
  • Use AI algorithms and predictive modeling to create dynamic optimization models that can quickly adapt to changing market conditions and customer needs
  • Mentor and train junior data scientists to develop analytical skills in the team
  • Stay up-to-date with the latest OR and AI techniques and explore innovative solutions to stay ahead of the competition
  • 10+ years of experience in the OR field with a strong understanding of the mathematical underpinning of optimization algorithms
  • Advanced degree (Master’s or in Industrial Engineering, Operations Research, Applied Mathematics, or related fields
  • Expertise in mathematical modeling, optimization, and simulation techniques
  • Experience in deploying OR solutions in the cloud environment
  • Experience with AI algorithms, predictive modeling and machine learning techniques and their application to insurance problem solving
  • Proficiency in programming languages such as Python, R, and SQL
  • Strong understanding of descriptive statistics and exploratory data analysis techniques
  • Excellent problem-solving and analytical skills with the ability to work with complex datasets
  • Strong communication and collaboration skills to work effectively with different stakeholders and cross-functional teams
  • Experience in leading end-to-end data science lifecycle from problem definition to model implementation
  • Track record of successfully delivering impactful optimization projects in the insurance / finance / retail industries
  • Ability to mentor and train junior data scientists to develop analytical skills in the team
  • Self-driven with a passion for staying up-to-date with the latest OR and AI techniques and exploring innovative solutions to stay ahead of the competition.

At Chubb we are committed to providing equal employment opportunities to all employees and applicants. It is our policy to provide equal employment opportunities to employees and applicants based on job-related qualifications and ability to perform a job. If you require an accommodation during the hiring process or upon hire, please inform Human Resources. If a selected applicant requests accommodation during the recruitment process, Chubb will consult with the applicant in order to provide suitable accommodation that takes into account the applicant’s accessibility needs.