Operations Automation Specialist

About Lantern

Lantern is the specialty care platform connecting people with the best care when they need it most. By curating a Network of Excellence comprised of the nation's top specialists for surgery, cancer care, infusions and more, Lantern delivers excellent care with significant cost savings to employers and their workforces. Lantern also pairs members with a dedicated care team, including Care Advocates and nurses, for the entirety of their care journey, helping them get back to good health, back to their families and back to work. With convenient access to specialists nationwide, Lantern means quality care is within driving distance for most. Lantern is trusted by the nation's largest employers to deliver care to more than 6 million members across the country. Learn more about us at lanterncare.com.

Job Overview

As an Operations Automation Specialist, you will personally resolve provider inquiries — and then build the tools that make resolving them one-by-one unnecessary.

Today, resolving a provider's question well requires detailed manual auditing and review across multiple systems. It's careful work, and it doesn't scale. The data needed to answer these questions faster and more accurately already exists across our data sources — and modern AI tools give us the opportunity to bring that work together and scale it in a way we haven't yet.

You'll start by doing the job as it exists today: working real provider inquiries, learning the systems, and building the judgment that comes from firsthand experience with the problem. From there, your job shifts toward building — using AI-assisted workflows to resolve inquiries faster, surface patterns worth fixing at the root, and move providers toward getting their answers without needing to contact us at all. This is a new kind of role for Lantern, built specifically because this problem is ready for it.

This is a technical role in the lowercase-t sense: you don't need to arrive as a software engineer, and this isn't the data-analyst track either — it's for people with the instinct to spot patterns and automate them away, even without the formal technical background yet to do it at scale. For the right person, it's a deliberate stepping stone toward more technical roles at Lantern over time.

We're deliberately not writing this as a data-engineering job. Someone who already codes fluently can thrive here, but so can a business-minded person with real communication skills and the drive to build — the second profile is just as much the target as the first.

We're not narrowing this to one experience level. A recent grad with the right instincts is a big part of who we want in the pipeline — but so is someone with five or ten years of practical business experience who has been deliberately building toward exactly this kind of role. More time doing the work often means sharper pattern recognition, not a worse fit. We'll teach you the domain — you don't need to know claims or healthcare going in. What we won't do is teach you SQL or Claude Code step by step; the expectation is that you go figure new tools out largely on your own, the same way people have always picked up new tools on the job.

What You'll Do:

Resolve, hands-on

Work a live queue of provider claims inquiries — status, payment amount, denial and rejection questions, aged-claim batches — through to accurate resolution

Audit individual claims across systems to confirm they were submitted, validated, priced, and paid correctly

Escalate genuine pricing or appeals determinations, and document what you learn so the pattern can eventually be automated

Understand the problem at scale

Query claims, pricing, and provider data directly using SQL — you'll pick up our specific tools (including Databricks) on the job — to characterize inquiry volume, root causes, and cost to serve — much of which isn't measured today

Separate inquiries caused by real payment problems from inquiries caused by providers simply lacking visibility — they need different fixes

Build the fix

Design and build AI-assisted workflows that triage inquiries, retrieve the relevant claim and contract facts, validate payments, and draft accurate, provider-ready responses

Build in verification, not just automation — a confident wrong answer about a payment amount is worse than a slower correct one

Test against real ticket volume, measure impact honestly, and iterate

Push upstream

Where a category of inquiry traces back to a fixable upstream issue — deferred claims, ingestion errors, notification gaps — work the root cause, not just the symptom

Contribute to provider self-service so fewer questions need to reach us in the first place

About You:

We're looking for someone with the business and communication fundamentals to do this job well today, plus the eagerness to build the automation that makes it unnecessary tomorrow. This is not a data-engineering posting — a business background, a support/ops background, or a fresh grad with the right instincts can all be a great fit, provided the willingness to learn is real.

Core

You can communicate clearly and confidently with providers and internal stakeholders — you can pick up the phone, have a real conversation, and explain what's happening in plain terms, not just write it up

You have (or can quickly build) a working understanding of the business and financial side of how claims and payments move — you're not purely a technical person who can't connect the dots to what the business needs

You have a genuine desire to expand your SQL skills and to use Python and AI tools — including agentic coding tools like Claude Code — to improve how work gets done, even if you're not there yet; you'd rather build something than just talk about it

You think carefully about correctness: your instinct is to ask “how would I know if this were wrong,” and then build the check

You're comfortable in ambiguity, in an environment where the documentation is thin and the systems don't always agree with each other

You start by actually doing the resolution work — the same work our team does well every day — because you can't build good automation for a process you haven't done yourself; understanding it that closely is what makes the eventual automation worth trusting

Strongly preferred

Hands-on SQL and/or Python experience, and real fluency with modern AI tools — including agentic coding tools like Claude Code — with something you've actually built with them to show for it

A year or more of work experience — internship, part-time, or full-time — especially something that required real conversations with people (customers, providers, clients)

Background or coursework in process improvement — comfortable getting into how a workflow actually operates today, spotting where it breaks, and proposing a fix

A business, finance, economics, or business-intelligence academic background — these programs tend to build in real SQL (and often some Python and AI-tool) exposure alongside the communication and financial-literacy skills this role needs

Exposure to revenue cycle, claims, payer operations, or provider billing — deep expertise isn't required, we'll teach the domain, but prior contact with how claims get paid shortens the ramp meaningfully

Experience in a support, service, or operations function, with an instinct for what the person on the other end actually needs

Not required

A formal software engineering background or computer science degree — you're not building distributed systems, you're using tools that already exist to make an operational process dramatically better

Already being an expert in SQL, Python, or AI tooling — what we're looking for is real curiosity and a track record of teaching yourself new tools; we'll give you real problems, context, and room to grow into them, not a step-by-step curriculum

Hands-on experience with Databricks or other enterprise data-engineering