Interested in this Data Scientist role at Solace Health?
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About This Role
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
At Solace, data isn't just about understanding the past; it's about predicting the future. We are a rapidly scaling startup where we work hard, move fast, and wear many hats.
The Solace Data Team is looking for a Data Scientist who excels at forecasting, prediction, and machine learning to help us anticipate patient needs and optimize our operations. Reporting directly to the VP of Data, you will have the opportunity to lead high-impact projects that directly influence our strategic decision-making.
In this role, you will go beyond descriptive analytics to build the predictive engines that drive our business. Whether you are forecasting operational volume, modeling patient risk, or designing rigorous A/B tests, you will bridge the gap between advanced statistics and practical business application, ensuring our models are robust, interpretable, and impactful.
What You’ll Do
- Build Predictive Models: Develop and refine machine learning models to solve critical business problems, such as predicting patient churn, estimating operational volume, or scoring advocate quality.
- Drive Forecasting Rigor: Own the development of time-series forecasts that guide our capacity planning and resource allocation. You will help us anticipate "what comes next" with higher accuracy.
- Design & Analyze Experiments: Lead the design and analysis of A/B tests and causal inference studies. You will ensure we measure the true impact of our product and operational changes.
- Uncover Deep Insights: Apply advanced statistical methods to complex datasets to find patterns that simple reporting might miss. You will answer the "why" and "how" behind our most difficult questions.
- Productionize Solutions: Work closely with Data Engineers to take your models from a local notebook to a production environment, ensuring they run reliably within our data infrastructure.
- Communicate Complexity: Translate complex statistical findings into clear, actionable narratives for non-technical stakeholders. You will be the voice of statistical reason in strategic discussions.
What You Bring
- Statistical Expertise: You have a strong background in statistics, probability, and mathematics (e.g., hypothesis testing, regression analysis, time-series forecasting).
- Python & ML Proficiency: You are fluent in Python and its data science ecosystem (pandas, scikit-learn, statsmodels, NumPy). You write clean, reproducible code.
- Forecasting Experience: You have hands-on experience with time-series analysis and forecasting techniques (e.g., ARIMA, Prophet, exponential smoothing).
- Advanced SQL: You can write complex queries to wrangle your own data from Snowflake without relying solely on others to prepare it for you.
- Preferred Masters Degree or PHD: Data Science, Applied Science, or related fields.
- Data Privacy Awareness: You understand the importance of protecting patient data. You are familiar with best practices for handling PHI and PII and ensuring privacy in your analysis.
- Startup DNA: You are a self-starter who is comfortable with ambiguity. You take ownership of problems and are willing to wear many hats to get the job done.
Bonus Points
- Deployment Experience: Experience using tools like Docker, Airflow, or MLflow to deploy and monitor models in production.
- Healthcare Experience: Experience working with healthcare or insurance claims data is a plus.
- NLP & Unstructured Data: Experience applying Natural Language Processing (NLP) techniques to extract insights from unstructured text data.
- Marketplace Matching: Experience designing matching algorithms or ranking systems for two-sided marketplaces.
- dbt & Engineering Skills: Comfort reading or writing dbt models to understand the lineage of your data.
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 [email protected] or [email protected].
Compensation Range: $160K - $200K
Salary Context
This $160K-$200K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →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 8,943 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($180K) sits 5% below the category median. Disclosed range: $160K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Solace Health AI Hiring
Solace Health has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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