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Opmed.ai is a rapidly growing healthcare AI startup helping leading health systems optimize their operations. Our platform is already in use at major health systems and large networks such as Mayo Clinic and Geisinger.
We're looking for a Data Science Tech Lead to take technical and delivery ownership of this team, a hands-on senior IC role. You'll set research direction, own client-facing delivery end-to-end, and drive how the team builds and scales its use of GenAI tooling and agentic capabilities. You'll work closely with other data scientists & analysts and partner daily with Solutions, Implementation, and Engineering. This role comes with real latitude to shape research direction and influence our product offering and roadmap - not just execute against a backlog.
What you'll do
1. Research Direction
Set the technical research agenda for our tabular prediction models.
Guide how we incorporate temporal signal - case history, lag features, schedule snapshots- without defaulting to time-series techniques that don't fit our data.
Own our strategy for extracting value from unstructured data sources via LLMs - feature extraction, embeddings, or fine-tuning - balancing accuracy, cost, and constraints like latency and privacy.
Set standards for experimentation, evaluation, and model guardrails.
2. Client Project Delivery
Own DS delivery end-to-end across concurrent client engagements - from discovery through modeling, validation, and go-live.
Partner with Solutions and Implementation on discovery and rollout; represent Data Science in client meetings, presenting results directly to stakeholders.
Ensure delivery lands inside the product, on timeline, coordinating with Engineering on productionization.
Track delivery risk across the client portfolio, flagging blockers and scope changes early.
3. GenAI Tooling & Team Capability
Build the team's onboarding and delivery playbook for new use-cases and encode it as reusable Claude skills/agents
Turn repeatable steps in the DS workflow into standardized, shared tooling.
Raise the team's GenAI fluency through mentoring on Claude Code and related tooling.
Requirements
M.Sc. in Computer Science, Statistics, Engineering, or a related quantitative field - or equivalent practical experience (PhD is an advantage).
Experience building or adapting LLM-native ML research workflows - including monitoring research quality and running critical peer reviews of results.
6+ years in applied data science / ML, including recent experience as a senior IC technical lead.
Deep, practical expertise with tabular ML: gradient-boosted trees (XGBoost/LightGBM or similar), feature engineering, and rigorous model evaluation for regression and classification problems.
Comfortable working with temporal/panel data (lag features, entity history, point-in-time snapshots).
Proven ownership of client-facing technical engagements end-to-end - discovery, stakeholder presentations, and delivery against deadlines - ideally in a B2B / enterprise or professional-services-adjacent setting.
Hands-on experience building and shipping GenAI applications: LLM-based feature extraction or structured extraction from unstructured text, prompt engineering vs. fine-tuning trade-offs, and at least conceptual fluency with agent orchestration.
Track record of building tooling or frameworks (internal libraries, standardized pipelines, or similar) that scaled a team's output, not just personal productivity.
Strong communicator who can move fluidly between technical peers, engineering, and non-technical clinical or business stakeholders.
Comfortable holding multiple concurrent workstreams and client engagements with competing timelines.
It will be great if you have
Healthcare, clinical, or EHR data experience - HL7/FHIR familiarity, PHI-aware ML practices, or exposure to clinical NLP (e.g., ClinicalBERT-style models).
Experience fine-tuning or distilling LLMs (SFT, LoRA/QLoRA) for domain-specific extraction or embedding tasks.
Direct experience building Claude Code skills/agents or working within an agentic-tooling ecosystem (LangChain, LangGraph, or equivalent).
Background in causal inference or experimentation design - useful for rigorously measuring model and product impact, even if not core to the day-to-day.
Experience in a scale-up environment where Data Science sits close to client delivery rather than behind a platform/API boundary.
Why join us
At Opmed.ai you will see your work deployed in real hospitals and health systems, improving the daily lives of clinicians and patients. You will lead the heart of our technology, collaborate with a talented and mission driven team, and work with world class partners and health networks. If you want to build world class AI and optimization at scale and help shape the future of healthcare operations, we would love to talk.