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Data

Data Scientist

Solace · United States · full-time · Remote

Datafull-timeRemoteUnited StatesUSD 160,000 – 200,000 / yearly
Data Science & AnalyticsDirector
RemoteJobs editorial overview

Strategic Overview

Data Scientist at Solace is defined by the responsibilities and candidate requirements in the employer's listing.

The published role emphasizes work such as 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.

The role is listed for United States with a remote working arrangement, giving candidates the practical context needed to assess fit before applying.

Job seeker snapshot

Key information at a glance

Role focusData
LocationUnited States
WorkplaceRemote
Employmentfull-time
SalaryUSD 160,000 – 200,000 / yearly
PostedNov 6, 2026
Last seenNov 6, 2026
Role context

About This Role

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.

What the role delivers

Key Responsibilities & Objectives

  • 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.
Candidate blueprint

Required Qualifications & Candidate Blueprint

  • Statistical Expertise: You have a strong background in statistics, probability, and mathematics (e.g., hypothesis testing, regression analysis, time-series forecasting).
  • Forecasting Experience: You have hands-on experience with time-series analysis and forecasting techniques (e.g., ARIMA, Prophet, exponential smoothing).
  • Preferred Masters Degree or PHD: Data Science, Applied Science, or related fields.
Additional fit signals

Preferred Qualifications

  • 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.
Compensation & benefits

Salary & Benefits

Published compensation: USD 160,000 – 200,000 / yearly

Live market comparison

Salary Benchmark

This role’s published compensation is compared with active employer-disclosed ranges for the same role, in the same currency and pay period.

This jobUSD 160,000 – 200,000 / yearly
Current market range$13,000 – $536,000 · per year
Comparable jobs66

Benchmark uses active employer job postings in our database, excludes this listing, and matches the currency and pay period. It is not a third-party salary estimate; currencies are not converted.

Employer-provided source content

Full Job Description

The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.

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 recruiting@solace.health or advocate@solace.health.

RemoteJobs original analysis

Editor’s Market Perspective (Our Custom Analysis)

Why This Position Matters

This opening may suit candidates who can demonstrate experience relevant to the employer's published requirements. Its strongest career signal is the scope of the work itself — particularly 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. — rather than a generic title alone.

Resume & Application Strategy

For the application, connect specific examples from your experience to the responsibilities and qualifications in the employer's description. Quantify outcomes from comparable work, and place the most relevant evidence near the top of the resume. When the employer explicitly names tools, platforms, leadership scope, or domain experience, use those source terms accurately and in context.

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