This role will help accelerate commercial group insurance membership using data & in-depth analysis to improve marketing campaign strategy.
Open to NYC, Wellesley MA, and Hartford CT locations.
- Senior marketing analytics consultant advising on marketing campaign strategy supporting Business-to-Consumer and/or Business-to-Business programs
- Engage with commercial & marketing stakeholders on a regular basis to identify opportunities to implement data science solutions
- Lead large, cross-functional team with Marketing, Media, Agency, etc. to design, setup and optimize multi-channel marketing campaigns (email, digital, direct mail, etc.)
- Conduct in-depth post-campaign analysis to understand key performance drivers and make actionable recommendations
- Develop dashboards & self-service visualization tools to enable data-driven decision making in marketing
- Build & maintain deep understanding of the commercial group insurance business environment and the key drivers of growth
- Performs analyses of structured and unstructured data to solve multiple and/or complex business problems utilizing advanced statistical techniques and mathematical analyses
- Use strong programming skills to explore, examine and interpret large volumes of data in various forms
- Campaign experience - campaign analytics/stakeholder
- Minimum 5 years relevant business and analytical experience
- 3+ years of experience leading analytics initiatives with track record of business impact
- 3+ years of experience using programming tools such as R, SQL, SAS or Python
- In-depth knowledge of marketing campaign design methodologies and techniques: A/B and experimental test design, holdout control setup; building and using segmentations and propensity models, etc.
- Anticipates and prevents problems and roadblocks before they occur
- Demonstrates strong ability to communicate technical concepts and implications to business partners
- Tableau or similar visualization tool experience preferred
- Digital advertising / marketing data analysis experience preferred