Senior Manager, Member Engagement & Optimization

Everlywell is a digital health company pioneering the next generation of biomarker intelligence—combining AI-powered technology with human insight to deliver personalized, actionable health answers. We transform complex biomarker data into life-changing insights—seamlessly integrating advanced diagnostics, virtual care, and patient engagement to reshape how and where health happens. Over the past decade, Everlywell has delivered close to 1 billion personalized health insights, transforming care for 60 million people and powering hundreds of enterprise partners. In 2025, an estimated 1 in 94 U.S. adults received an Everlywell test, solidifying our spot as the #1 at-home testing company in the country. Fueled by AI and built for scale, we’re breaking down barriers, closing care gaps, and unlocking a more connected healthcare experience that is smarter, faster, and more personalized. We are hiring a strategic, marketing-forward leader to serve as the end-to-end owner of member communications and engagement across Everlywell's health plan and enterprise programs. This role owns the full picture: the strategy, engagement packages we take to market, and the outcomes those programs drive — with a core mandate of materially driving member clinical activation and return rates across our book of business. This role leads engagement strategy across all channels, including email, SMS, direct mail, AI-powered IVR, and live agent outreach. It reports into the Marketing organization and will play a key role in shaping the future of member engagement at Everlywell. This person should bring strong healthcare marketing judgment and meaningful experience designing engagement and activation programs in payer, Medicare Advantage, Medicaid, or broader health plan environments. They will partner closely with Product, Operations, Analytics, ML / AI, Clinical, Compliance, and client teams to improve targeting, optimize cadence and channel mix, and drive member action through both engagement design and use of predictive models.