Machine Learning Senior Advisor - HIH - Evernorth

Job description

ABOUT EVERNORTH:  Evernorth℠ exists to elevate health for all, because we believe health is the starting point for human potential and progress. As champions for affordable, predictable and simple health care,we solve the problems others don’t, won’t or can’t.  Our innovation hub in India will allow us to work with the right talent, expand our global footprint, improve our competitive stance, and better deliver on our promises to stakeholders. We are passionate about making healthcare better by delivering world-class solutions that make a real difference. We are always looking upward. And that starts with finding the right talent to help us get there. We're a small AI-native R&D team inside one of the largest health organizations in the country. We build products that affect real healthcare outcomes — how risk gets detected, how care decisions get made, and how clinical teams do their work. We're looking for hybrid builders who own what they build: people who decide what to build, not just how. AI fluency isn't nice-to-have. It's how we work. A few honest things about this team: we have access to clinical data at a scale that would take a startup year to build through partnerships, and the ability to deploy to millions of people from day one. We also sit inside an enterprise, which means some things take longer than they would at a seed-stage company. We've structured the team to minimize that friction — and we're direct about where it still shows up. Position overview: The Problems We're Solving Turning overwhelming data into clarity. Large organizations generate more signals than any team can manually process. A leadership team responsible for a portfolio of millions needs to know which three trends demand attention this week — and why they're happening — before manual reports would surface them. We build systems that find what matters, understand why it matters, and tell the story to the people who need to act — in near-real-time, not weeks later. The technical challenge is multi-layered: anomaly detection, causal reasoning, natural language synthesis. The human challenge is harder: making AI-generated insight compelling and trustworthy enough that people actually change what they do. Clinical AI that reaches real patients. The patients who most need intervention are often invisible until they're already in crisis. We build tools that help care teams identify who needs attention now — turning clinical data into prioritized, actionable signals for the people making care decisions. The data is messy, the stakes are real, and getting it wrong matters. Rapid prototypes that drive real decisions. New AI opportunities surface constantly. Leadership needs to evaluate them fast — before the window closes. We run short, high-intensity build cycles: from "what if we could..." to a working prototype that can actually move a decision. Scoping what to build is harder than building it, and this is where product sense matters most. Making the team itself faster. We build AI-augmented workflows that compound over time — tools that reduce overhead, accelerate research, and let the team stay in flow on the hard problems. We eat our own cooking: every internal tool gets used by the people who built it before it goes anywhere else. The infrastructure that makes all of it possible. Enterprise data is siloed and hard to connect to AI systems. We build the connective tissue — agent infrastructure that makes real organizational data accessible to the tools we're building. This is the foundation everything else depends on. How We Work • Spec-driven, not sprint-driven. Write a spec, build it, ship it, demo it. No standups, no ceremonies, no two-week batches. • Weekly demos. Every Friday. Working software over status updates. • High autonomy. You own workstreams, not tickets. No one tells you which file to edit. • AI-native every day. We use the tools everyone else is still debating. That's not optional — it's how this team operates. What We Look For Dimension What It Looks Like Product sense Think about users first. Asks "why" before "how." Has opinions about what to build — and what not to. Engineering fluency Full-stack capable. Can move from data to API to UI as the problem demands. Writes production-quality code. Design eye Creates usable interfaces without a designer for every decision. Knows good UX when they see it. AI nativity Use AI tools as core infrastructure for daily work — coding, research, and validation. Understands where models are good and where they break. Shipping velocity Track record of finishing things. Ships products, not just pull requests. Ownership mindset Owns outcomes, not tasks. Takes initiative without being told. "That's not my job" isn't in their vocabulary. We want builders who are deep in one or two areas and fluent across many — able to touch any part of the stack when the problem demands it. Qualities That Matter Quality What It Looks Like Craftsmanship Sweats details that matter. Ignores ones that don't. Pragmatism Ships 80% solutions. Knowing when good enough is good enough. Curiosity Learning new tools and tech eagerly. Not precious about their stack. Low ego Celebrates team wins. Admits mistakes. Asks for help. Nice Genuinely kind. Not nice-as-performance. Non-negotiable — small teams r