Senior Data Scientist

Job Description

JOB DESCRIPTION

Job Title: Senior Data Scientist

Experience Level: 6-10 years

We are seeking a strong candidate with significant experience in the insurance sector and a strong background in data science to fill an exciting Senior Data Scientist position at Chubb. The Senior Data Scientist is expected to be a subject matter expert in Predictive Modelling and Machine Learning Algorithms and will work closely with business stakeholder to deliver impactful solutions, drive adoption, and articulate value.

Responsibilities

• Spearhead the design and development of machine learning models, with a keen eye for practical application and great intuition for its performance in production.
• Deploy production-ready solutions ensuring robustness, scalability, and alignment with business goals.
• Collaborate closely with ML Engineers to design scalable systems and model architectures that enable real-time ML/AI services.
• Articulate intricate data science concepts and findings to a varied audience, ensuring clarity for both technical and non-technical stakeholders.
• Review the team's deliverables before sharing them with business stakeholders, including codes, presentations.
• Coach individuals in the team and build a high-performance workplace.
• Proactive plan and manage projects, anticipate product integration and drive thought leadership in bringing the best ML practices from the industry.
• Collaborate with the Business stakeholders, product owners and other data teams to build impactful solutions to business problems.
• Define key performance metrics that accurately reflect the value delivered to end-users.
  
  

Qualifications

Desired Qualifications

• Minimum 6 years of hands-on experience in data science, with a proven track record in deploying ML models in production environments.
• A bachelor’s or master’s degree, preferably in Statistics, Mathematics, Analytics or Computer Science.
• Experience in the insurance sector with an understanding of industry-specific data challenges.
• Strong foundation in a variety of machine learning techniques, including but not limited to ensemble methods, decision trees, and regression analysis.
• Advanced proficiency in Python and its data science libraries (e.g., pandas, sci-kit-learn, TensorFlow).
• Excellent presentation and communication skills, with the ability to effectively convey complex findings to both technical and non-technical stakeholders.
• Prior experience in working directly with the business stakeholders.