FBS Data Scientist (Customer & Agent Solutions)

Capgemini; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Private Health Insurance

Pension Plan

Paid Time Off

Training & Development

Grocery vouchers

Job summary

OurClient in the United States is seeking a Data Scientist to develop predictive and prescriptive analytics, driving insights for customer and agent solutions. You will combine deep ML and statistical skills with strong communication to influence business decisions.

Responsibilities include deploying scalable models, partnering with stakeholders, and mentoring teammates, while working across CAS to improve retention and operational performance.

Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, Business Analytics, Computer Science, or a related quantitative field.
• 3+ years of experience in Data Science, Advanced Analytics, Machine Learning, or a related field.
• Experience with: Python (4-6 years), Advanced SQL (7+ years), GitHub (1-3 years), Microsoft Office Suite (7+ years), Databricks exposure (1-3 years).
• Fluent English communication skills.

Responsibilities

• Develop and deploy predictive and prescriptive analytics models to support customer and agent-related business initiatives.
• Apply machine learning, statistical analysis, and data mining techniques to solve complex business problems.
• Analyze large datasets to uncover trends, opportunities, and actionable recommendations.
• Design, build, and maintain scalable, production-ready analytical solutions.
• Partner with business stakeholders to translate objectives into analytical approaches and success metrics.
• Collaborate with Data Engineering, IT, and Analytics teams on model deployment and optimization.
• Present findings and recommendations to technical and non-technical audiences.
• Support business areas focused on customer retention, servicing, operational performance, lead optimization, and agent effectiveness.
• Mentor junior team members and contribute to analytics best practices.

Skills

Python

SQL

Machine Learning

Statistics

GitHub

MS Office

Databricks

Fluent English

Education

Bachelor's degree in Data Science/Statistics/CS

Tools

Databricks

GitHub

Microsoft Office

Job description

OurClientis one of the United States’ largest insurers,providinga 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 andnearly18,500employees. Finally, ourClientis part of one the largest Insurance Groups in the world.

About the Role

The Data Scientist will be responsible for developing predictive models, advanced analytics solutions, and actionable insights that support strategic decision-making across Customer & Agent Solutions (CAS). This role partners closely with business stakeholders to identify opportunities, solve complex business problems, and drive measurable improvements in customer retention, agent experience, operational efficiency, and overall business performance.

The ideal candidate combines strong technical expertise in machine learning, statistics, Python, and SQL with the ability to communicate insights to non-technical audiences and influence business outcomes.

Key Responsibilities

• Develop and deploy predictive and prescriptive analytics models to support customer and agent-related business initiatives.
• Apply machine learning, statistical analysis, and data mining techniques to solve complex business problems.
• Analyze large datasets to uncover trends, opportunities, and actionable recommendations.
• Design, build, and maintain scalable, production-ready analytical solutions.
• Partner with business stakeholders to translate business objectives into analytical approaches and success metrics.
• Collaborate with Data Engineering, IT, and Analytics teams on model deployment and optimization.
• Utilize ML Ops best practices, version control, and code documentation standards.
• Present findings and recommendations to technical and non-technical audiences.
• Support business areas focused on customer retention, servicing, operational performance, lead optimization, and agent effectiveness.
• Mentor junior team members and contribute to analytics best practices.

Required Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, Business Analytics, Computer Science, or a related quantitative field.
• 3+ years of experience in Data Science, Advanced Analytics, Machine Learning, or a related field.
• Experience with:
  • Python (4-6 years preferred)
  • Advanced SQL (7+ years preferred)
  • GitHub (1-3 years preferred)
  • Microsoft Office Suite (7+ years preferred)
  • Databricks exposure (1-3 years preferred)
• Intermediate experience with Machine Learning.
• Intermediate experience with Statistical Analysis, Hypothesis Testing, and Descriptive Statistics.
• Experience developing and translating analytical solutions into business outcomes.
• Fluent English communication skills.

Preferred Qualifications

• Master's degree in Data Science, Statistics, Mathematics, Analytics, or related field.
• Experience within P&C Insurance, Banking, Financial Services, or similar highly regulated industries.
• Experience supporting customer service operations or call center environments.
• Experience with call routing optimization, workforce optimization, or contact center analytics.
• Exposure to Databricks and modern cloud-based analytics platforms.
• Knowledge of ML Ops, GitHub, and model deployment lifecycle management.
• Experience with developing classification and other supervised learning models.

Competitive compensation and benefits package:

1. Competitive salary and performance-based bonuses
2. Comprehensive benefits package: Grocery vouchers, saving funds, SGMM, etc.
3. Career development and training opportunities
4. Dynamic and inclusive work culture within a globally renowned group
5. Private Health Insurance
6. Pension Plan
7. Paid Time Off
8. Training & Development

Note: Benefits differ based on employee level.

Personal Data Processing: The personal data provided during the recruitment and selection process will be collected, processed, and retained for legitimate recruitment and compliance purposes, in accordance with applicable data protection and privacy laws and FBS internal policies.

Legal Authorization to Work: Employment with FBS is conditional upon the candidate having valid, local legal authorization to work in the country where the role is based at the time of hire. FBS does not sponsor or obtain work authorization unless explicitly stated.

Exclusivity of Employment and Conflict of Interest: Upon acceptance of an offer and during employment with FBS, employees will not be permitted to engage in parallel employment, professional activities, or paid work for other entities. Any ownership, partnership, directorship, or participation in other businesses or companies must be fully disclosed and formally reviewed in accordance with FBS internal conflict‑of‑interest and external engagement policies prior to the start date or as soon as such circumstances arise.

Failure to comply with these conditions may impact the hiring decision or employment continuation.