Level

Solace Health

Associate Data Scientist (College Grad 2027)

AI in this role

scikit-learn
ml-opsnlp

About Solace

Healthcare in the U.S. is fundamentally broken. The system is so complex that 88% of U.S. adults do not have the health literacy necessary to navigate it without help. Solace cuts through the red tape of healthcare by pairing patients with expert advocates and giving them the tools to make better decisions—and get better outcomes.

We're a Series C startup, founded in 2022 and backed by Inspired Capital, Craft Ventures, Torch Capital, Menlo Ventures, Signalfire, and IVP. Our U.S. based team is lean, mission-driven, and growing quickly.

Solace isn't a place to coast. We're here to redefine healthcare—and that demands urgency, precision, and heart. If you're looking to stretch yourself, sharpen your edge, and do the best work of your life alongside a team that cares deeply, you're in the right place. We’re intense, and we like it that way.

Read more in our Bloomberg funding announcement here.

About the Role

This is a rare chance to learn data science from the inside out at one of the fastest-growing healthcare startups in the country. As an Associate Data Scientist at Solace, you'll work on the forecasting and machine learning systems that operate our marketplace in real time: matching patients with advocates, predicting demand, and keeping the business ahead of its own growth.

You won't spend your first year re-deriving textbook exercises. You'll contribute to real models with mentorship from senior data scientists, and you'll be expected to think independently, question your own results, and defend your methodology. You don't need years of experience, but you do need an innate ability to get things done.

This role is for the ambitious, the curious, and those who don't shy away from feedback. If you're the kind of person who sees a problem and can't resist solving it, keep reading.

This is an in-person role for new college graduates in our Redwood City office, 3 days a week. Start date: July 2027.

What You'll Do

  • Contribute to real predictive models in your first weeks: forecasting, matching, and scoring systems that the business runs on.

  • Pull and wrangle your own data with SQL from Snowflake. Nobody hands you a clean CSV here.

  • Support the design and analysis of A/B tests and experiments, learning how to measure the true impact of product and operational changes.

  • Work alongside data engineers to see how models get from a notebook into production, learning MLOps practices as you go.

  • Apply statistics with rigor: knowing when a result is real, when it's noise, and how to tell the difference.

  • Present your findings and defend your methodology to technical and non-technical audiences. Your voice matters here, even on day one.

  • Grow your craft: learn how to frame problems, validate models, design experiments, and make smart modeling decisions.

What You Bring to the Table

  • A bachelor's or master's degree in Statistics, Data Science, Computer Science, Applied Math, or a related quantitative field (or equivalent practical experience), graduating between December 2026 and June 2027.

  • Strong fundamentals in statistics and probability: hypothesis testing, regression, and the judgment to know when your model is fooling you.

  • Working Python skills, including the core data science ecosystem (pandas, scikit-learn, NumPy), plus working SQL.

  • Projects that show you can do real quantitative work: research, internships, Kaggle competitions, hackathons, or personal modeling projects. We care about what you've built and what you learned from it, not just what you studied.

  • A bias toward action. You have an exceptional craving for momentum, and you'd rather ship a simple model that works than polish a complex one that never launches.

  • Curiosity for how systems work, comfort with ambiguity, and a refined palate for controlled chaos.

  • Confident communication. You can explain a model to someone who has never heard of one, and take critique of your methodology without defensiveness.

  • A sense of care. You take pride in the details, because in healthcare data, the details are the difference.

  • A hunger to learn and an ego small enough to admit when you don't know something.

Bonus Points

  • Exposure to time-series forecasting (ARIMA, Prophet, exponential smoothing).

  • Coursework or projects in causal inference or experimental design.

  • Familiarity with dbt, Airflow, Docker, or cloud environments (AWS).

  • Experience with NLP or unstructured text data.

Up for the Challenge?

We look forward to meeting you.

Fraudulent Recruitment Advisory: Solace Health will NEVER request bank details or offer employment without an interview. All legitimate communications come from official solace.health emails or ashbyhq.com. Report suspicious activity to recruiting@solace.health.

Applicants must be based in the United States.

Up for the Challenge?

We look forward to meeting you.

Fraudulent Recruitment Advisory: Solace Health will NEVER request bank details or offer employment without an interview. All legitimate communications come from official solace.health emails only or ashbyhq.com. Report suspicious activity to recruiting@solace.health or advocate@solace.health.

How we rate this

Associate Data Scientist (College Grad 2027) at Solace Health rates 13 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsNLPscikit-learn

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. What NLP problem have you worked on, and how did you measure whether it actually worked?
  3. What are the limits of scikit-learn that you've run into, and how did you work around them?

Adapt your resume

  • List these exact terms on your resume: ML Ops, NLP, and scikit-learn. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.

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