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About This Role
About Solace
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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
At Solace, data isn't just about understanding the past; it's about predicting the future and running the systems that operate our marketplace in real time. We're looking for a technical Data Science Manager to lead and grow our Data Science team.
In this role, you will manage and mentor a team of 4\-6 data scientists, owning their growth and the team's roadmap end to end. Reporting to the VP of Data, you will be the connective tissue between Data Science and the rest of the business, working closely with Engineering, Analytics, Product, Marketing, and Operations to prioritize what the team works on and see it through to impact. Your team owns two distinct threads of work: predictive analytics that keeps the business ahead of its own growth, and the production machine learning that powers our core product. At Solace, we hold managers to a high technical bar: you need to be credible enough with your team to go deep, not just manage from above.
What You'll Do
- Coach and Develop the Team: Manage and mentor data scientists, giving them the feedback and growth opportunities to build stronger judgment and take on more ownership.
- Own the Roadmap: Set and drive execution of the Data Science roadmap, acting as the intake point for cross\-functional requests and making deliberate prioritization decisions.
- Build Cross\-Functional Relationships: Partner closely with leaders to ensure that data science is embedded in how decisions get made, not layered on after the fact.
- Drive Marketplace Forecasting: Own the forecasts and targets that keep advocate capacity, funnel volume, and supply and demand aligned, giving the business clear numbers to plan and hire against.
- Own Production ML Systems: Own the machine learning models that run inside our core product, including matching and quality scoring, from prototype through production.
- Establish Best Practices: Set the standard for how data science gets done at Solace, including code review, experimentation design, model validation, and monitoring.
What You Bring
- 6\+ Years of Data Science Experience: Proven track record building and shipping predictive models and machine learning systems, ideally at a product\-led tech startup or marketplace.
- 2\+ Years of Management or Leadership Experience: Direct experience managing, mentoring, or leading data scientists or a similar technical team.
- Deep Python, ML \& SQL Fluency: Fluent in Python, its data science ecosystem, and the advanced SQL needed to pull your own data from Snowflake. You can read, debug, and meaningfully critique your team's code and models.
- Production ML Experience: You have taken models from notebook to production, with experience monitoring, detecting drift, and iterating on models running live.
- Statistical \& Forecasting Expertise: Strong background in statistics and time\-series forecasting (e.g., ARIMA, Prophet).
- Experimentation Rigor: Strong grounding in A/B testing and causal inference, with a track record of designing experiments that produce trustworthy results.
- Exceptional Communication: You can translate complex modeling work into clear narratives for non\-technical executives and cross\-functional partners.
- Startup DNA: You thrive in ambiguity, move with urgency, and are comfortable wearing multiple hats.
Bonus Points
- Marketplace Matching Experience: Experience designing matching algorithms or ranking systems for multi\-sided marketplaces.
- Healthcare Experience: Experience working with healthcare or insurance claims data.
- MLOps Tooling: Experience packaging, deploying, and monitoring models in production, including containerization (Docker), experiment tracking and model registries (e.g. MLflow, SageMaker), and CI/CD for model deployment.
- dbt \& Engineering Skills: Comfort reading or writing dbt models to understand data lineage.
- Advanced Degree: Master's or PhD in Data Science, Statistics, Applied Math, or a related field.
*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.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Solace Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Solace Health AI Hiring
Solace Health has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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