Interested in this Data Scientist role at Veracyte, Inc.?
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About This Role
At Veracyte, we offer exciting career opportunities for those interested in joining a pioneering team that is committed to transforming cancer care for patients across the globe. Working at Veracyte enables our employees to not only make a meaningful impact on the lives of patients, but to also learn and grow within a purpose driven environment. This is what we call *the Veracyte way* – it's about how we work together, guided by our values, to give clinicians the insights they need to help patients make life\-changing decisions.
Our Values:
- We Seek A Better Way: We pursue bold ideas, embrace complexity, and keep pushing forward.
- We Make It Happen: We act with urgency, deliver with excellence, and always find a way.
- We Are Stronger Together: We engage with empathy, align around what's best for Veracyte, and celebrate as one team.
- We Care Deeply: We show up with integrity, kindness, and respect for one another.
### The Position:
We are seeking a talented and experienced Principal Data Scientist with proven expertise in developing oncology\-based digital pathology AI models (DPAI) to join our Veracyte's Data Science team. This position offers a unique opportunity to work with skilled professionals to drive Veracyte's DPAI\-based research program. Successful candidate is expected to lead algorithm development of new diagnostic products based on digital pathology by leveraging one of the world's largest multi\-modal oncology databases.
Key Responsibilities:
- Develop DPAI models using WSI data to predict clinical outcomes and pathological/morphologic features ensuring their biological explainability, including adapting open\-source state\-of\-the\-art AI foundation models to Veracyte's and collaborator
- Identify the correct data sources for developing DPAI models and lead team efforts to obtain annotation or labels for ML development.
- Analyze complex data/ML problems and break them into actionable sub projects or tasks. Coordinate the delegation of tasks to junior team members as technical lead, track progress and ensure correctness of the deliverables.
- Collaborate with both internal and external partners to understand the clinical and business requirements for given products and tailor projects and algorithms accordingly.
- Partner closely with bioinformatics, statistical, and medical experts to document projects, including writing and generating analyses and visualizations for publication in peer\-reviewed journals.
- Lead collaboration within and across teams with the ability to manage project execution and completion.
- Coordinate with both internal and external medical experts to identify critical research questions and datasets that build evidence for the utility of our models.
### Who You Are:
- PhD in Data Science, Machine Learning, Applied Math or equivalent field.
- 8\+ years of experience of data/applied scientist role or equivalent in disease\-related field, (cancer preferred).
- Expert in Python or equivalent language for AI/ML development in the context of computer vision / DPAI (this includes data manipulation and preparation).
- Proficient in statistical analysis, especially in survival modelling and hypothesis testing (i.e., multivariate regression modelling with interaction effects).
- Experience working in cloud computing environments (AWS preferred).
- Demonstrated proficiency in summarizing and communicating findings from data, including attention to detail when sharing findings.
- Demonstrated ability to clearly explain AI/ML concepts to both experts and non\-experts in text and figures via formal presentations and academic writing.
- Ability to work effectively in a fast\-paced and collaborative environment.
- Eagerness to learn new technologies and adapt to evolving requirements.
- Direct technical leadership and employee management experience, including the ability to break down complex problems into actionable and delegable projects.
- Passionate about data, independently eager to learn, and possesses strong analytical, problem\-solving, and story\-telling skills.
RELEVANT EXPERIENCE (preferred but not required):
- Experience serving as technical leader or manager.
- Proficiency with documentation and submission in regulated diagnostic environments (LDT or IVD).
- Experience working with real world clinical data and evidence.
LOCATION:
- This is a remote friendly position for both US and Canada based candidates.
- On\-site desk\-space is available in South San Francisco or San Diego.
\#LI\-Remote
For candidates working remote (US), the range is $188,000 \- $210,000
For candidates working in Canada, the range is:$160,000 \- $182,000
### What We Can Offer You
Veracyte is a growing company that offers significant career opportunities if you are curious, driven, patient\-oriented and aspire to help us build a great company. We offer competitive compensation and benefits, and are committed to fostering an inclusive workforce, where diverse backgrounds are represented, engaged, and empowered to drive innovative ideas and decisions. We are thrilled to be recognized as a 2024 Certified™ Great Place to Work® in both the US and Israel \- a testament to our dynamic, inclusive, and inspiring workplace where passion meets purpose.
### About Veracyte
Veracyte (Nasdaq: VCYT) is a global diagnostics company whose vision is to transform cancer care for patients all over the world. We empower clinicians with the high\-value insights they need to guide and assure patients at pivotal moments in the race to diagnose and treat cancer. Our Veracyte Diagnostics Platform delivers high\-performing cancer tests that are fueled by broad genomic and clinical data, deep bioinformatic and AI capabilities, and a powerful evidence\-generation engine, which ultimately drives durable reimbursement and guideline inclusion for our tests, along with new insights to support continued innovation and pipeline development. For more information, please visit www.veracyte.com or follow us on LinkedIn or X (Twitter).
Veracyte, Inc. is an Equal Opportunity Employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability status. Veracyte participates in E\-Verify in the United States. View our CCPA Disclosure Notice
If you receive any suspicious alerts or communications through LinkedIn or other online job sites for any position at Veracyte, please exercise caution and promptly report any concerns to [email protected]
Salary Context
This $160K-$210K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Veracyte, Inc., 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $160K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Veracyte, Inc. AI Hiring
Veracyte, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in San Diego, CA, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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