Decision Scientist

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 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

1. Competitive salary and performance-based bonuses