Data Scientist
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.
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