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About This Role
Surescripts serves the nation through simpler, trusted health intelligence sharing, in order to increase patient safety, lower costs and ensure quality care . We deliver insights at critical points of care for better decisions — from streamlining prior authorizations to delivering comprehensive medication histories to facilitating messages between providers.
Job Summary:
The Senior Data Scientist - Healthcare Data will play a pivotal role in developing and implementing data-driven solutions across various business functions, collaborating with cross-functional teams to analyze complex data sets, derive actionable insights, and drive strategic decision-making. The Senior Data Scientist will design, deliver, and optimize powerful insights and visuals with advanced analytics and AI, including effective use of AI assistants within development tools to accelerate planning, solution design, implementation, and documentation.
The Senior Data Scientist is expected to possess expert business, data, and analytics expertise to handle complex advanced analytics initiatives. The Senior Data Scientist is self-motivated and able to work under limited supervision from management, can manage multiple complex and significant projects simultaneously, and is responsible for making connections and integrating work across teams and departments. Working closely with data solutions architects and other leaders, the Senior Data Scientist ensures alignment of prioritization of work and appropriate allocation of resources, while applying responsible, secure, and privacy-aware AI-assisted workflows (human-in-the-loop review, traceability, and validation of AI-generated outputs).
This role will lead efforts around process improvement to enhance team effectiveness in leveraging advanced analytics tools and techniques in our Google Cloud environment. In support of the Data Literacy Program, the Senior Data Scientist will be responsible for developing training and information materials on new techniques/tools/reports across the enterprise to ensure rapid and effective adoption of insights derived from advanced analytics techniques.
Responsibilities:
- Data Visualization: Create compelling visualizations to communicate findings and insights to stakeholders.
- Collaboration: Work closely with business leaders, product managers, data scientists, and engineers to translate business requirements into analytical solutions.
- AI-Assisted Planning & Design: Use AI assistants to accelerate requirements clarification, solution options, and technical design; convert outputs into reviewed artifacts (design docs, user stories, test plans).
- AI-Assisted Implementation: Use AI coding assistants to draft code/queries/notebooks/pipelines; perform human review for correctness, security, performance, and maintainability; follow IP/licensing and data-use policies.
- QA Oversight: Define acceptance criteria, test strategy, and validation methods (data checks; model metrics and bias/robustness as applicable); partner with engineering/QA on regression coverage and release readiness.
- AI-Teaming & Refinement: Iteratively refine solutions using prompt engineering, grounding/source practices, and evaluation rubrics; document prompts, assumptions, and decisions for repeatability.
- Mentorship: Provide guidance and mentorship to other data scientists, analysts and engineers within the team. Serves as a mentor/role model, imparting analytic knowledge, experience, and skills to other staff at all levels, either individually or as a member of project teams.
- Continuous Learning: Stay abreast of industry trends, emerging technologies, and best practices in data science.
- Evangelism: Evangelize within company the capabilities and opportunities that data science and advanced analytics empower towards corporate goals.
- Question, validate, and perform quality assurance of the data for integrity and consistency to support ongoing data quality assessment and improvement initiatives.
- Understand all applicable data privacy and security laws, rules, regulations, and contractual restrictions, and follow all Surescripts data governance and data usage rights policies and procedures.
- Document access and use requirements for advanced analytics data/reporting products and support the definition of report template and specifications.
- Demonstrate ability to communicate complex analytic results in a clear and concise fashion to sponsors/clients at all levels, and to audiences of all sizes.
- Summarize and synthesize large bodies of work down to the essential elements and convey those results effectively and efficiently, in both written and oral form, to the most senior leaders in client organizations.
- Effectively communicate with & engage colleagues at all levels of the organization.
- Effectively delegate responsibilities to the appropriate people and levels.
- Develop internal and external networks of contacts and have a positive influence on those networks.
Play a key role in supporting corporate initiatives and support senior leadership initiatives to realize Surescripts' goals. *
Qualifications:
Basic Requirements:
- Master’s degree in Mathematics, Computer Science, Statistics, or other related field; or equivalent experience
- 5+ years of experience in data science, data management and/or applying data analysis and reporting skills in a business context
- 2+ years of experience with large healthcare transactional datasets / reporting
- Expertise in programming languages to facilitate analysis (e.g. R, Python or MATLAB)
- Expertise with SQL /PLSQL, Relational and NoSQL databases, and Structured and Unstructured data
- Analytical background and research experience with large volumes of personal data
- Ability to translate statistical analysis into a written and verbal presentation for non-data science audience.
- Experience in statistical modeling using healthcare data
- Experience developing training material and delivering training to user groups of 10 or more
- Knowledge of privacy laws and regulations around health data (HIPAA)
- Demonstrated proficiency using AI assistants across planning, design, implementation, and documentation (e.g., IDE/code assistants such as GitHub Copilot or Claude Code; chat-based assistants such as Microsoft Copilot; notebook/workbench assistants), with human review, traceability, and validation of AI-generated outputs.
- Demonstrated experience providing QA oversight for analytics/ML deliverables (defining acceptance criteria, designing validation approaches, partnering with engineering/QA on test automation and release readiness).
Experience with AI-teaming techniques for refining technical solutions (prompt engineering, grounding/citation practices, evaluation rubrics and benchmarking, and documentation of AI-assisted decisions), including identifying and mitigating common failure modes (e.g., hallucinations, data leakage, insecure code). *
Preferred Qualifications:
- PhD degree in a quantitative field (e.g., Data Science, Statistics) or MBA
- Health IT industry knowledge with a special focus on e-prescribing
- Expertise in statistical analysis and rigor, statistical software (e.g., R, SPSS, or SAS)
- Deep experience with large language models and/or natural language processing (e.g., fine-tuning, prompt/program orchestration, retrieval-augmented generation, or LLM evaluation frameworks) beyond baseline AI-assistant usage.
- Familiarity with responsible AI practices (privacy, security, data handling, model risk considerations, and controls for AI-generated code/content) in regulated environments.
- Project management experience
- Google Cloud environment expertise
- Experience with Google Cloud; comparable experience with AWS, Azure, Databricks, or Snowflake will also be considered.
Experience with MLOps frameworks such as GCP, DataBricks, or SnowFlake. *
Keywords: data scientist, predictive analytics, machine learning AI, healthcare data
Surescripts embraces flexibility through its Flexible Hybrid Work model for most positions. This model allows employees to work virtually while still utilizing our offices as collaboration centers. With alignment and agreement from your leadership, you can come and go from the office as needed.
To be considered for employment, applicants must have a valid U.S. work authorization allowing work without restrictions with Surecripts in the U.S. At this time, we are unable to provide support or provide sponsorship for immigration benefits such as work visas. Additionally, we do not participate in academic training programs or work-study programs through an academic institution that require employer endorsement of F-1/CPT or F-1/STEM.
Why Wait? Apply Now
We’re a midsize company. This means you’re not just another employee ID number. Here, you can build real relationships and feel supported by truly awesome people with diverse backgrounds and talents in an innovative and collaborative work culture. We strive to create an environment where you can b e yourself, share your ideas and work your way. We offer opportunities for employee development, as well as competitive compensation packages and extensive benefits.
At Surescripts, base pay is one part of our Total Rewards Package (which may also include bonus, benefits etc.) and is determined within a range. The base pay range for this position is $157,500 - $192,500 per year. Your base pay may vary within or outside of this range depending on a number of factors, including (but not limited to) your qualifications, skills, experience, and location.
Benefits include, but are not limited to, comprehensive healthcare (including infertility coverage), generous paid time off including paid childbirth and parental leave and mental health days , pet insurance, and 401(k) with company match and immediate vesting. To learn more, review the Keep You and Yours Healthy , Balancing Work and Life , and Where Talent Takes Shape links under the Better Benefits. Better Work. Better Life section of our careers site.
Physical and Mental Requirements
While performing duties of this job, an employee may be required to perform any, or all of the following: attend meetings in and out of the office, travel, communicate effectively (both orally and in writing), and be able to effectively use computers and other electronic and standard office equipment with, or without, a reasonable accommodation. Additionally, this job requires certain mental demands, including the ability to use judgement, withstand moderate amounts of stress and maintain attention to detail with, or without, a reasonable accommodation.
Surescripts is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate on the basis of race, color, religion, age, national origin, ancestry, disability, medical condition, marital status, pregnancy, genetic information, gender, sexual orientation, parental status, gender identity, gender expression, veteran status, or any other status protected under federal, state, or local law.
Salary Context
This $157K-$192K 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 Surescripts, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($175K) sits 8% below the category median. Disclosed range: $157K to $192K.
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.
Surescripts AI Hiring
Surescripts has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $192K - $192K.
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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