Founding Applied Data Scientist ($100k-$200k + Equity) at Bluevia Health at Jack & Jill/External ATS

, , United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jun, 26

Salary

200000.0

Posted On

14 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Data Pipelines, Feature Sets, Longitudinal Event Data, Temporal Reasoning, EHR Data, Claims Data, XGBoost, LLM-Extracted Features, Multimodal Representations, Clinical Data Standardization, Predictive Modeling

Industry

Staffing and Recruiting

Description

This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. Founding Applied Data Scientist ($100k-$200k + Equity) at Bluevia Health Company Description: Bluevia Health - Mayo Clinic partnered AI startup Job Description: As the founding data scientist at this early-stage healthcare AI company, you will own the end-to-end loop from raw clinical data to deployable models. Working with petabyte-scale EHR data from the Mayo Clinic, you’ll build the foundations for predicting post-surgical complications, directly impacting patient outcomes across a 21-site US health system. Why this role is remarkable: Rare access to 10M+ longitudinal patient records and 5.5 petabytes of rich clinical data through a live Mayo Clinic partnership. Massive real-world impact with an accelerated deployment timeline into a major 21-site US health system already underway. High-ownership founding role in a lean team of four, where your technical decisions define the company’s core AI platform. What you will do: Build standardization pipelines and foundations to transform raw, messy EHR tables into analysis-ready longitudinal datasets. Design and validate rule-based computed phenotypes for clinical complications across diverse specialties and patient subgroups. Develop and benchmark predictive models, starting with XGBoost and iterating toward LLM-extracted features and multimodal representations. The ideal candidate: Strong Python and SQL skills with proven experience building data pipelines and feature sets from complex, raw sources. Expertise in longitudinal event data and temporal reasoning, preferably within a healthcare, EHR, or claims data context. High-agency problem solver who can move from ambiguous clinical questions to rigorous, validated, and deployable model outputs. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI Career Agent that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. Next steps Step 1. Visit our website. Step 2. Click 'Talk to Jack'. Step 3. Talk to Jack so he can understand your experience and ambitions. Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role. Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction. Step 6. If not, Jack will find you excellent alternatives. All for free. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

Responsibilities

The founding data scientist will own the end-to-end process from raw clinical data to deployable models, focusing on building standardization pipelines to transform messy EHR tables into analysis-ready datasets. Responsibilities also include designing and validating computed phenotypes for clinical complications and developing/benchmarking predictive models.