Lead Operational Research Data Scientist

CHUBB; Montreal; On-site; CAD 100,000 - 130,000; Full time

Job summary

Chubb, a leader in insurance, is seeking an Operational Research Data Scientist to lead optimization initiatives using advanced OR techniques and AI. The role involves developing models, analyzing data, and mentoring junior team members to enhance operational efficiencies in the insurance industry.

Qualifications

• 10+ years of experience in Operations Research.
• Expertise in optimization algorithms and mathematical foundations.
• Experience deploying OR solutions in cloud environments.

Responsibilities

• Develop and implement optimization models to improve business performance.
• Analyze data for insights in insurance risk profiling and claims management.
• Collaborate with cross-functional teams to design and integrate solutions.

Skills

Mathematical modeling

Optimization

Data analysis

Problem-solving

Communication

Education

Advanced degree (Master’s or higher) in Industrial Engineering, Operations Research, Applied Mathematics

Tools

Python

R

SQL

Job description

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 global local operations.

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 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 applying mathematical models to solve complex business problems.

Major Responsibilities:

1. Develop and implement optimization models to improve business performance and operational efficiencies in the insurance industry.
2. Analyze large volumes of data and provide insights for data-driven decisions in insurance risk profiling, claims management, and other key areas.
3. Collaborate with cross-functional teams including actuaries, underwriters, and IT to design, develop, and integrate optimization solutions.
4. Lead the entire data science lifecycle from problem definition to model deployment.
5. Utilize AI algorithms and predictive modeling to create dynamic optimization models that adapt to changing market conditions and customer needs.
6. Mentor and train junior data scientists to enhance analytical skills within the team.
7. Stay updated with the latest OR and AI techniques and explore innovative solutions to maintain competitive advantage.

Qualifications:

1. 10+ years of experience in Operations Research with a strong understanding of optimization algorithms' mathematical foundations.
2. Advanced degree (Master’s or higher) in Industrial Engineering, Operations Research, Applied Mathematics, or related fields.
3. Expertise in mathematical modeling, optimization, and simulation techniques.
4. Experience deploying OR solutions in cloud environments.
5. Proficiency in programming languages such as Python, R, and SQL.
6. Strong knowledge of descriptive statistics and exploratory data analysis.
7. Excellent problem-solving and analytical skills with complex datasets.
8. Effective communication and collaboration skills to work with diverse stakeholders.
9. Proven track record of delivering impactful optimization projects in insurance, finance, or retail sectors.
10. Ability to mentor and develop junior team members.
11. Self-driven with a passion for staying current with OR and AI advancements.

At Chubb, we are committed to providing equal employment opportunities. We support accessibility needs during the hiring process and upon employment. Please inform Human Resources if accommodations are needed.