Capgemini

    FBS Data Scientist

    Capgemini
    Posted 11/14/2025Senior Level
    Full-time
    Data & Analytics
    Data Science
    Consumer Analytics
    Retention Models
    Agency Economics
    Lead Optimization

    Job Description

    Our Client is one of the United States’ largest insurers, providing a wide range of insurance and financial services products with gross written premiums well over US$25 Billion (P&C). They proudly serve more than 10 million U.S. households with more than 19 million individual policies across all 50 states through the efforts of over 48,000 exclusive and independent agents and nearly 18,500 employees. Finally, our Client is part of one the largest Insurance Groups in the world. Role Description Executes on moderate level business challenges involving data science. Succeeds in projects by scoping, defining measures of success, utilizing a data science vision for project success, and accomplishes successfully within prescribed timelines. Executes on intermediate level projects with a sense of urgency. Utilizes knowledge of consumer analytics including retention models, agency economics, and lead optimization in their daily work. Utilizes moderate knowledge of programing, complex ETL and specialized modeling methods to execute projects. Demonstrates clean reusable code and effective documentation, encourages other to do the same. Partners closely with IT, business, and data management/engineering teams to understand, utilize, and improve our data infrastructure. Advises on fundamental concerns and serves as an objective and transparent partner to drive fact-based decision making and a measures of success culture. Develops presentations and presents to leadership. Occasionally communicates complex technical material understandable to non-technical associates. Manages moderate model deployments via MLOps techniques. Works with analytics and IT teams to deploy models/rules in various platforms and support testing of new solutions. Mentors junior data science team members. Provides coaching and knowledge around technical and non-technical skills. Bachelor´s Degree in Data Science, Statistics, Mathematics, Business Analytics or related. Full English proficiency At least 5 years of experience working on large-scale structured and unstructured multidimensional data using moderately advanced knowledge of open-source cloud-enabled analytical programming languages and strong experience and knowledge of data analysis manipulation tools (SQL, Python, Snowflake) and cloud computing services (AWS). Working knowledge of data visualization tools Deep understanding of deploy models/rules in various platforms and support testing of new solutions. Predictive Modeling Machine Learning Statistical Analysis P&C Knowledge & Operations (rating making, reserving, claims, distribution, etc.) - Basic Python SQL AWS Cloud Tools PowerBI is a plus Insurance Background is a plus

    This position comes with competitive compensation and benefits package:

    Competitive salary and performance-based bonuses Comprehensive benefits package Career development and training opportunities Flexible work arrangements (remote and/or office-based) Dynamic and inclusive work culture within a globally renowned group Private Health Insurance Pension Plan Paid Time Off Training & Development About Capgemini Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of over 340,000 team members in more than 50 countries. With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group €22.5 billion in revenues in 2023.

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