FBS Decision Scientist (MMM)

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.

What to expect on your journey with us

A solid and innovative company with a strong market presence

A dynamic, diverse, and multicultural work environment

Leaders with deep market knowledge and strategic vision

We are seeking a Decision Scientist with experience in Marketing Mix Modeling (MMM) and advanced analytics to join our team. This role will play a key part in measuring marketing effectiveness, forecasting business performance, and generating actionable insights that support strategic decision-making and optimize marketing spend.

Team Function

The Marketing Data Science and Measurement team supports the media team in optimizing media investment. It develops and maintains measurement and attribution models to assist in decision making and budget allocation.

Role Description

This role will be responsible for

• Maintaining and improving our in-house Marketing Mix Model (MMM)

• Presenting results from the MMM leadership and stakeholders to inform channel allocation and budget planning.

• Creating forecasts of expected future sales to allow for adequate preparation and optimal investment.

• Developing other forms of marketing measurement to provide a full picture of marketing effectiveness.

The ideal candidate will have deep knowledge of statistical and machine learning techniques and will be comfortable interpreting results of analyses to stakeholders across a wide range of seniority, from entry-level to C-suite. They will be able to clearly communicate actionable insights that will improve marketing efficiency.

Requirements

• Experience with MMM (Marketing Mix Models).

• At least 3 years of experience in analytics and/or Data Science, preferably in marketing and/or MMM.

• Knowledge of statistics and Machine Learning.

• Experience working with large and complex datasets.

• Working knowledge of R or Python.

• Bachelor’s degree in a technical field.

• Fluency in English

Critical Skills

Statistical and machine learning concepts and tools - Advanced

Marketing Media Mix Modeling – Advanced

Communication - able to clearly communicate actionable insights that will improve marketing efficiency.

Software / Tool Skills

Python or R, SQL

Benefits

• Competitive salary and performance-based bonuses

• Comprehensive benefits package

• Career development and training opportunities

• Flexible work arrangements (remote)

• Dynamic and inclusive work culture within a globally renowned group

• Private Health and Dental Insurance

• Pension Plan

• Meals tickets

• Life Insurance

By applying for this position, candidates acknowledge and agree that

• 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.