FBS Decision Scientist

Our Client is 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 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

to drive marketing measurement and media effectiveness through incrementality testing, causal inference, and advanced analytics. This role will partner with cross-functional stakeholders to deliver actionable insights that optimize media investments and improve business outcomes.

Team Function

The Media Measurement & Analytics team supports the media teams in executing and optimizing media budgets by providing analytical rigor to investment decisions. The team executes incrementality tests, builds measurement models, and reports on performance.

Role Description

This role will be responsible for:

• Planning and implementing incrementality tests
• Analyzing output of MMM models
• Developing other forms of marketing measurement to provide a full picture of marketing effectiveness.
• Communicating results to leadership and stakeholders.

The ideal candidate 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

• At least 3 years of experience in Analytics and/or Data Science.
• Bachelor’s degree in a technical field.
• Fluency in English
• Experience working with large and complex datasets.
• Experience applying causal inference tools (Difference-in-Differences, synthetic controls, propensity score matching) to real-life problems.
• Experience with experimental design and incrementality testing.
• Working knowledge of R or Python, SQL, and PowerBI.

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:

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• 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._

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• 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._

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• 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.

• At least 3 years of experience in Analytics and/or Data Science.
• Bachelor’s degree in a technical field.
• Fluency in English
• Experience working with large and complex datasets.
• Experience applying causal inference tools (Difference-in-Differences, synthetic controls, propensity score matching) to real-life problems.
• Experience with experimental design and incrementality testing.
• Working knowledge of R or Python, SQL, and PowerBI.