We are looking for an experienced Senior Data Scientist with years of Life Insurance experience to join our fast-paced, high-energy team. As a Senior Data Scientist, you will perform quantitative data analysis to enable organizational decision making and develop solutions to complex business problems and create value to the business. This position offers exposure to a wide variety of analytics tools and technologies as well as unique challenges in problem-solving. Building and deploying predictive models in support of our pricing strategy will be a focus area.

In This Role, You Will

• Synthesize data to uncover inherent trends, assess impact of data on business usage, and to make recommendations for improvement.
• Build GLM pricing models in support of various property and casualty insurance products.
• Research, recommend, and implement statistical and other mathematical methodologies appropriate for the given business problem.
• Build explanatory and forecasting models using descriptive and inferential statistics, and regression analysis.
• Perform data validation, preprocessing, and exploratory data analyses.
• Communicates analytical goals, approach, outcomes, and insights to both technical and non-technical audiences.
• Translate analytical findings into relevant business insights.
• Collaborate with business partners and peers within the organization to understand and scope the problem, gather business requirements, and develop robust model solutions that drive improvement in key business metrics.
• Effectively communicate with key stakeholders (both technical and non-technical) in written, oral and presentation formats.
• Create/Maintain excellent working relationships with business partners across the Chubb organization including Product, Actuarial, IT, and analytics peer groups.
• Provide guidance and mentorship directly and indirectly to junior team members.

QUALIFICATIONS

• Extensive experience with both supervised and unsupervised machine learning algorithms, including GLMs (Generalized Linear Models), with a strong focus on model interpretability and business impact.
• Strong understanding of predictive and prescriptive analytics, statistical modeling, and data visualization techniques.
• Extensive experience with data manipulation, feature engineering, and working with large, complex datasets.
• Hands-on experience with exploratory data analysis, model development, and end-to-end solution design.
• Significant programming experience in Python for data analysis, modeling, and production-ready solutions.
• Significant programming experience in SQL, including writing complex queries and optimizing performance.
• Experience working in cloud-based data and analytics platforms, particularly Azure (e.g., Azure Data Lake, Azure Synapse, Azure ML).
• Hands-on experience using Databricks for data engineering, analytics, and machine learning workflows.
• Working knowledge/familiarity with Git version control and collaborative development practices.
• Ability to communicate effectively with both technical and non-technical audiences in written, oral, and presentation formats. 
• Demonstrated ability to multi-task, learn new technologies quickly, and apply strong analytical and problem-solving skills in a fast-paced environment.

Preferred

• Prior experience in property and casualty insurance and/or working knowledge of actuarial concepts and insurance pricing methodologies.
• Advanced knowledge of model tuning, validation, evaluation, and operationalization in production environments.
• Experience deploying and maintaining models using cloud-native tools and MLOps practices, preferably within Azure and Databricks ecosystems.
• Experience architecting, developing, and consuming APIs at scale.
• Experience with Deep Learning frameworks and libraries such as TensorFlow/Keras, PyTorch, or MXNet.
• Strong hands-on experience with big data technologies and distributed computing frameworks (e.g., Databricks, Spark).
• Experience working with modern cloud platforms such as Azure (preferred), AWS, or GCP, and data platforms such as Snowflake.
• Experience with Text Analytics and Natural Language Processing techniques.
• Comfort working in command-line environments (Linux and/or Windows) and automating workflows via scripting.
• Experience with at least one additional programming language beyond Python, such as R, Scala, Julia, Java, Go, or C++.

Education: 

• The candidate should have 3+ years of industry experience in data science, analytics, or a related quantitative field.
• Graduate degree in a technical discipline such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics, or Engineering is preferred.

OPPORTUNITIES AND CHALLENGES:

• Opportunity to work in a professional, modern and energizing setting.
• Challenging projects, possibilities, cutting-edge technology, and problems.
• An open and dynamic working atmosphere that promotes the interchange of
  ideas, while also empowering you to work and create in your own style. Each
  employee's talent and accomplishments are valued, and outstanding employees
  are recognized and rewarded on a yearly basis.
• Opportunities for capacity development, as well as assistance with professional
  certification expenses, are available to those who work in the field (1 million - 5
  million).
• Some advanced certifications will cover the full cost of studying and taking the
  exam.

ATTRACTIVE REWARDING POLICY AND WORK-LIFE BALANCE:

• Review salary 1-2 times/year, with the possibility of an unexpected salary rise
  based on capacity an infinite number of times per year
• Policy for 13th-Month Salary Bonus and Holiday Bonuses According to
  Company Regulations.
• Holiday travel mode (package 5-7 million/person), team building,…
•  Full participation in social insurance, as well as a yearly health checkup at a
  reputable hospital.