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About This Role
CareDx is a leading precision medicine diagnostics company advancing care in transplant, specialty oncology, and cell therapy. Through our innovative portfolio of molecular diagnostics, digital health solutions, AI-powered data and analytics, and patient support services, we partner with healthcare providers, patients, and biopharma organizations to help inform clinical decision-making and improve patient outcomes.
At CareDx, every employee has the opportunity to contribute to innovations that help transform patient care and improve lives.
The Data Scientist II will develop clinically grounded, interpretable AI and analytics solutions that support commercial, medical affairs, clinical research, and enterprise decision-making. The role will translate integrated structured and unstructured data into reliable insights, actionable recommendations, and reusable analytical products.
This role combines applied data science, statistical modeling, machine learning, generative AI, and research collaboration. The Data Scientist II will own defined workstreams from problem framing and research design through modeling, validation, documentation, deployment support, and stakeholder communication. Success requires strong quantitative judgment, reproducible development practices, and the ability to communicate methods, limitations, and recommendations clearly to technical, scientific, clinical, and business stakeholders.
Responsibilities
- Own analytics and AI workstreams for next-best-action and field education capabilities, translating integrated data into actionable recommendations.
- Advance literature intelligence workflows, including search, retrieval, synthesis, evaluation, and validation of AI-generated outputs.
- Develop interpretable machine learning, statistical, and generative AI solutions using structured and unstructured clinical, laboratory, commercial, and enterprise data.
- Build reproducible workflows and reusable components that can progress from research prototypes to reliable applications and decision-support tools.
- Lead analytics research proposals and collaborations, including cohort definition, analysis planning, modeling, interpretation, and communication of results.
- Partner with clinicians, scientists, medical affairs, research teams, and business stakeholders to translate questions into rigorous analytical plans and defensible results.
- Support abstracts, presentations, and research reports while applying appropriate privacy, security, validation, and documentation practices.
- Contribute to and provide backup support for forecasting, Promotion Effectiveness, and Digital Product Impact initiatives.
- Perform exploratory analysis, feature engineering, model development and evaluation, sensitivity testing, and scenario analysis using Python or R and SQL.
- Collaborate with data and application engineers on pipelines, version control, testing, deployment readiness, monitoring, and maintenance; clearly document data lineage, assumptions, limitations, and recommendations.
Qualifications
- Bachelor of Science (BS) or Master of Science (MS) degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field.
- 5+ years of experience in data science, machine learning, advanced analytics, AI engineering, or a related role, including 3+ years in healthcare, diagnostics, biotechnology, clinical research, life sciences, or another regulated environment.
- Strong hands-on proficiency in Python or R, SQL, and Git-based development workflows.
- Experience developing and validating predictive, descriptive, statistical, or machine learning models using real-world clinical, EHR, laboratory, research, or other complex longitudinal data.
- Ability to produce audit-ready documentation covering data lineage, methods, assumptions, testing, validation, and model limitations.
- Strong written and verbal communication skills and the ability to collaborate across technical, scientific, clinical, and business teams.
Preferred Qualifications
- Experience with AI-enabled search, natural language processing, literature intelligence, retrieval-augmented generation, or document synthesis.
- Experience moving analytical prototypes into user-facing or production workflows, including testing, monitoring, and ongoing support.
- Familiarity with Databricks, cloud data platforms, model lifecycle management, and modern analytics application frameworks.
- Experience contributing to clinical research deliverables such as protocols, abstracts, presentations, manuscripts, or analytical reports.
How We Work
- Curiosity: Start with Why + We understand why a project matters, what problem we are solving, whom it helps, and what success looks like. We ask thoughtful questions, align early, and move forward with confidence.
- Accountability: Extreme Ownership + We take responsibility for what we build and deliver. We communicate risks early, follow through on commitments, and help resolve problems rather than waiting for detailed direction.
- Transparency: Default to Share + We proactively share context, decisions, progress, and lessons learned. We document and demonstrate our work so others can build on it.
- Empowerment: Amplify Others + We succeed as a team. We collaborate openly, recognize the contributions of others, and give credit generously.
- Resilience: Learn Loudly + We treat mistakes and unexpected outcomes as opportunities to improve. We focus on solutions, identify root causes, and share what we learn.
Additional Details:
Every individual at CareDx has a direct impact on our collective mission to improve the lives of organ transplant patients worldwide. We believe in taking great care of our people, so they take even greater care of our patients.
Our competitive Total Rewards package for US Employees includes:
- Competitive base salary and incentive compensation
- Health and welfare benefits, including a gym reimbursement program
- 401(k) savings plan match
- Employee Stock Purchase Plan
- Pre-tax commuter benefits
- And more!
- Please refer to our page to view detailed benefits at https://caredx.com/company/careers
In addition, we have a Living Donor Employee Recovery Policy that allows up to 30 days of paid leave annually to a full-time employee who makes the selfless act of donating an organ or bone marrow.
With products that are making a difference in the lives of transplant patients today and a promising pipeline for the future, it's an exciting time to be part of the CareDx team. Join us in partnering with transplant patients to transform our future together.
CareDx, Inc. is an Equal Opportunity Employer-vets/disabled, and participates in the E-Verify program. By proceeding with our application and submitting your information, you acknowledge that you have read our U.S. Personnel Privacy Noticeand consent to receive email communication from CareDx. Certain jurisdictions require notice of how we use and protect your personal information. For more information, please read our Privacy Policy
The U.S. EEO posters are availablehere.
Note: We do not accept resumes from headhunters, placement agencies, or other suppliers that have not signed a formal agreement with us.
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Salary Context
This $108K-$135K range is in the lower quartile 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 CareDx, 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 ($121K) sits 36% below the category median. Disclosed range: $108K to $135K.
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.
CareDx AI Hiring
CareDx has 1 open AI role right now. They're hiring across Data Scientist. Based in Brisbane, CA, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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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