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About This Role
About Care Access
Care Access is working to make the future of health better for all. With hundreds of research locations, mobile clinics, and clinicians across the globe, we bring world-class research and health services directly to communities that often face barriers to care. We are dedicated to ensuring that every person has the opportunity to understand their health, access the care they need, and contribute to the medical breakthroughs of tomorrow.
With programs like Future of Medicine, which makes advanced health screenings and research opportunities accessible to communities worldwide, and Difference Makers, which supports local leaders to expand their community health and wellbeing efforts, we put people at the heart of medical progress. Through partnerships, technology, and perseverance, we are reimagining how clinical research and health services reach the world. Together, we are building a future of health that is better and more accessible for all.
To learn more about Care Access, visit www.CareAccess.com.
How This Role Makes a Difference
As a Senior Data Scientist, you will play a pivotal role in advancing Reify Health's data-driven solutions for clinical trials. In this position, you will drive the development of statistical models and machine learning algorithms to improve patient enrollment and trial management. You'll work in a highly regulated healthcare data environment, ensuring compliance with privacy standards while innovating on predictive analytics. This role involves close collaboration with cross-functional teams (especially ML Engineering) to translate complex data insights into practical, impactful tools for the clinical research community.
How You'll Make An Impact
- Site Randomization Forecasting: Develop/enhance forecasting models for site randomization and enrollment trends, enabling better planning and resource allocation across trial sites.
- Patient Matching/Ranking Algorithms: Support projects to build algorithms that intelligently match patients to (or rank patients for) appropriate clinical trials, enhancing recruitment efficiency and patient inclusion.
- Develop Other Advanced Statistical Models: Create and refine predictive models (Bayesian inference, regression analysis, time-series forecasting) to address other key clinical trial challenges and improve decision-making.
- AI Monitoring and Bias Detection: Implement processes to monitor machine learning models in production, detecting bias or performance drift and ensuring models remain fair, accurate, and compliant.
- Data Pipeline & Tooling Development: Build and optimize data pipelines and analytical workflows using tools like AWS Athena, Redshift, SageMaker, and dbt, enabling scalable model training and deployment.
- Regulatory Compliance in Data Science: Ensure all data science practices align with HIPAA, GDPR, and other privacy regulations, integrating compliance considerations into model development and data handling.
- Cross-Functional Collaboration: Work closely with machine learning engineers, product managers, and other stakeholders to integrate models into products and clearly communicate insights and recommendations.
The Expertise Required
- Statistical & ML Expertise: Strong foundation in statistical modeling and machine learning techniques, including experience with Bayesian methods, regression analysis, and time-series forecasting.
- Model Monitoring & Fairness: Proficiency in evaluating model performance and bias, with the ability to implement AI monitoring tools and bias mitigation strategies to ensure ethical and reliable outcomes.
- Technical Toolset: Advanced programming skills in Python (with libraries such as scikit-learn, PyMC, mlforecast, etc.) and SQL, as well as familiarity with data transformation tools like dbt.
- Cloud & Data Infrastructure: Hands-on experience with cloud-based analytics and ML services, especially AWS tools (Athena for querying, Redshift for data warehousing, SageMaker for model development/deployment).
- Regulated Data Handling: Experience working with sensitive healthcare or clinical trial data under regulations like HIPAA and GDPR, demonstrating a deep commitment to data privacy and security best practices.
- Collaborative Communication: Excellent teamwork and meticulous verbal/written communication abilities, with a track record of partnering with engineering and product teams to translate data science work into actionable business solutions.
- Domain Knowledge: Understanding of clinical research or health-tech environments is highly valuable, including insight into clinical trial operations and a passion for improving patient outcomes through data.
Certifications/Licenses, Education, and Experience:
- Minimum Education: + Minimum of Master's or Ph.D. in Statistics, Data Science, Computer Science, or a related quantitative field (or equivalent professional experience).
- Minimum Experience: + Minimum of 5+ years of hands-on data science or analytics experience, preferably in a healthcare, clinical research, or other highly regulated data environment.
How We Work Together
- Location: Remote within the United States. This role requires 100% of work to be performed in a remote office environment.
- Travel: This is a remote position with less than 10% travel requirements. Occasional planned travel may be required as part of the role.
- Physical demands associated with this position Include: The ability to use keyboards and other computer equipment.
The expected pay range for this role is $140,000 - $190,000 USD per year for full time team members.
Benefits & Perks (US Full Time Employees)
- Paid Time Off (PTO) and Company Paid Holidays
- 100% Employer paid medical, dental, and vision insurance plan options
- Health Savings Account and Flexible Spending Accounts
- Bi-weekly HSA employer contribution
- Company paid Short-Term Disability and Long-Term Disability
- 401(k) Retirement Plan, with Company Match
Mandatory Employer Disclosures:
Notice to Illinois applicants: Applicants are not obligated to disclose expunged juvenile records or adjudication, arrest, or conviction.
Notice to Connecticut applicants: Care Access may require applicants to submit to a urinalysis drug test in connection with an application for employment.
Notice to Arizona, Georgia, Indiana, and North Dakota applicants: Care Access complies with applicable laws prohibiting smoking in and around places of employment.
Notice to Massachusetts applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Notice to Rhode Island applicants: Care Access complies with Rhode Island law prohibiting smoking in enclosed areas within places of employment. Care Access is also subject to is subject to Chapters 29–38 of Title 28 of the Rhode Island General Laws.
Notice to Maryland applicants: UNDER MARYLAND LAW, AN EMPLOYER MAY NOT REQUIRE OR DEMAND, AS A CONDITION OF EMPLOYMENT, PROSPECTIVE EMPLOYMENT, OR CONTINUED EMPLOYMENT, THAT AN INDIVIDUAL SUBMIT TO OR TAKE A LIE DETECTOR OR SIMILAR TEST. AN EMPLOYER WHO VIOLATES THIS LAW IS GUILTY OF A MISDEMEANOR AND SUBJECT TO A FINE NOT EXCEEDING $100.
#LI-Remote
Diversity & Inclusion
We work with and serve people from diverse cultures and communities around the world. We are stronger and better when we build a team representing the communities we support. We maintain an inclusive culture where people from a broad range of backgrounds feel valued and respected as they contribute to our mission.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to, and will not be discriminated against on the basis of, race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
Care Access is unable to sponsor work visas at this time.
If you need an accommodation to apply for a role with Care Access, please reach out to: [email protected]
Salary Context
This $140K-$190K range is above the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Care Access, 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 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($165K) sits 13% below the category median. Disclosed range: $140K to $190K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Care Access AI Hiring
Care Access has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Boston, MA, US. Compensation range: $190K - $230K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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