Senior Data Scientist

Description

Senior Data Scientist

About Us

At Gifthealth, we're revolutionizing the way people experience healthcare by simplifying the process of managing prescriptions and health services. Our mission is to provide a seamless, personalized, and efficient healthcare experience for all our customers. We're a dynamic, innovative, and customer-centric company dedicated to making a positive impact on people's lives.

Position Summary

We are seeking a Senior Data Scientist to own traditional machine learning models that answer operational and patient-outcome questions, and support the evaluation of agentic AI workflows as our team begins to field generative workflows that act on model scores and real-time signals. This position plays a key role in supporting the AI/ML Engineering team and reports to the Sr.

Manager, AI/ML Engineering, ensuring alignment with organizational goals, operational excellence, and compliance standards.

This is an individual-contributor role with no direct reports today, though you will mentor newer teammates as the team grows. You'll join a small but growing cross-functional team with high autonomy and minimal ceremony. We are building new services and practices from the ground up, we write things down, and we invest in education, upskilling, and career growth.

We deliver quickly and iterate. Data is PHI. HIPAA, BAAs, masking, and role-based access are daily working realities here, not a compliance module you click through once.

Key Responsibilities

Models against operational decisions: own traditional machine learning models that answer questions such as which patients are likely to miss their next refill, switch pharmacies, or stall before checkout; which new prescriptions will stall in prior authorization; and how to optimize call volume, staffing demand, or distribution center throughput, driving both patient outcomes and operational improvements.

Agentic AI evaluation: own the evaluation mechanisms that prove agentic AI workflows are reliable, and infuse data-driven rigor into generative workflow development decisions such as foundation model selection and parameter optimization.

Training data sourcing and preparation: partner with data engineers on the pipelines your features depend on, drive sourcing of new data inputs, and own modeling data end-to-end, within a data platform built on Snowflake, a few hundred dbt models, Terraform, and Looker.

The data science practice: define how the team evaluates and validates a model, how it's documented, what's monitored, and what gets retired. Document best practices and mentor new data scientists as the team grows.

Qualifications

Education: A degree is optional; we evaluate demonstrated capability, not credentials.

Licensure/Certification: Not applicable.

Experience: Typically 4 to 7 years of experience, as a guide rather than a requirement.

Knowledge, Skills, and Abilities

You have built predictive models and shipped them into a business process, and you can name the decision each one served.

You have owned at least part of how your models run after launch: deployment, batch or real-time scoring, monitoring or retraining. Ideally on a cloud ML platform such as Snowflake ML or Amazon SageMaker.

Strong SQL and strong Python. Comfortable in a cloud warehouse and in dbt, or clearly able to get there fast.

Real statistical judgment: you know the difference between a model that validates well and a model that will hold up, and you can explain leakage, drift, and a badly chosen target to a non-technical stakeholder.

You have worked with LLMs seriously enough to be skeptical: you build evaluations, you know where hallucination bites, and you do not confuse a good demo with a working capability.

Demonstrated application of the above Qualification You are comfortable being the first: no playbook, no platform, an incomplete warehouse underneath you, and a need to pick the right first problem anyway.

You can say "this should not be a model" and mean it. Some of what looks like a data science problem here is a broken process or a missing field.

You exercise strong independent judgment and you mentor newer teammates. Both are level expectations in this role, and you will be the person others bring modeling questions to.

You use AI tools effectively, but your foundational coding and data science skills are sharp enough that you can read, audit, and debug every line yourself. If an agent writes it, you still own it and can defend it.

Nice to have: Snowflake, and especially Snowflake ML (Feature Store, Model Registry, Snowpark). This is a strong plus, as it's where our models are built and served today.

Nice to have: healthcare, pharmacy, payer, or life-sciences data, such as claims, prior authorization, benefit investigation, or adherence measurement.

Nice to have: generative AI platforms such as Snowflake Cortex AI or Amazon Bedrock.

Nice to have: working with PHI under HIPAA and BAAs, and the instincts that come with it.

Nice to have: time series and forecasting at operational cadence, for staffing or volume.

Nice to have: survival analysis or any honest treatment of censored data.

Nice to have: probabilistic record linkage or master data management.

Nice to have: experience introducing data science into a company that did not have it.

Work Environment

Location: Columbus, OH or remote (US).

Schedule: Full-time; standard business hours.

May require flexibility to support model monitoring, retraining, or evaluation work tied to operational decisions.

Regular collaboration with data engineers and the AI/ML Engineering team to ensure alignment.

Key Essential Functions

Must be able to work at a computer for extended periods Must be able to communicate effectively, verbally and in writing, with data engineers and non-technical stakeholders Must be able to handle and access PHI in compliance with HIPAA, BAA, masking, and role-based access requirements Must be able to read, audit, and debug code and models, including output produced with AI tools

Employment Classification

Status: Full-time

FLSA: Exempt

Equal Employment Opportunity (EEO) Statement Gifthealth is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. All employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, transgender status, national origin, age, disability, veteran status, or any other legally protected status.

We celebrate diversity and are committed to creating an inclusive environment for all employees. If you do not meet every requirement but still feel you would be a great fit for this role, we encourage you to apply!

Disclaimer

This job description is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of all responsibilities, duties, or skills required of personnel. Gifthealth reserves the right to modify job duties or descriptions at any time.