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About This Role
Location
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Remote U.S.
Employment Type
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Full time
Location Type
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Remote
Department
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Product Analytics
At Vanta, our mission is to help businesses earn and prove trust.We believe that security should be monitored and verified continuously, and we empower companies to practice better security and prove it with ease. Vanta has a kind and talented team, and while some have prior security experience, many have been successful at Vanta without it.
Our Data and Analyticsteam is currently looking for a Senior Data Scientist to join us!
You’ll be responsible for laying the foundation for a best\-in\-class business analytics function. You’ll partner closely with our business stakeholders to ensure that our analytics stack and processes meet the business needs today with an eye towards the future.
What you’ll do as a Senior Data Scientist at Vanta:
- Build and maintain trusted product data assets using dbt, Snowflake, and modern analytics infrastructure
- Leverage AI\-powered analytics tools and data agents (e.g., Snowflake Cortex) to accelerate insight generation, automate repeatable analysis, and scale decision\-making
- Define and evolve measurement frameworks for product health, customer lifecycle, and AI\-powered product experiences
- Partner closely with Product, Engineering, Design, and Customer Success to influence product strategy through data
- Help define Vanta’s analytics strategy and AI measurement practices as our product and data platform evolve
- Lead executive analytics reviews, translating complex analyses into clear recommendations that drive company decisions
How to be successful in this role:
- 4\+ years of experience working with data as a Data Scientist, Product Analyst, or Analytics Engineer in an applied business setting
- Strong foundation in SQL, Python (or R), statistics, and machine learning
- Experience designing and evaluating experiments, predictive models, and other statistical analyses to inform product decisions
- Experience building scalable data assets, metrics, and analytical frameworks on modern cloud data platforms (e.g., Snowflake, dbt)
- Deep experience with data visualization
- Experience using modern AI\-assisted analytics tools or LLM\-powered workflows to accelerate analysis and improve decision making
- Deep understanding of product analytics, customer behavior, and B2B SaaS growth metrics
- Experience partnering closely with Product, Engineering, Design, and Customer Success throughout the product lifecycle
- Demonstrated ability to translate ambiguous business problems into meaningful analytical frameworks and actionable recommendations
- Passion for building reusable analytical systems that increase organizational leverage rather than one\-off analyses
- Open to using AI to amplify their skills and strengthen their work \- demonstrating curiosity, a willingness to learn, and sound judgment in applying AI responsibly to improve efficiency and impact.
What you can expect as a Vanta’n:
- Industry\-competitive salary and equity
- Comprehensive medical, dental, and vision coverage, with 100% of employee\-only benefit premiums covered for most medical plans
- 16 weeks paid Parental Leave for all new parents
- Health \& wellness stipend
- Remote workspace, internet, and cellphone stipend
- Commuter benefits for team members who report to the SF and NYC office
- Family planning benefits
- Matching 401(k) contribution with immediate vesting
- Flexible PTO policy, plus 80 hours of Sick Time
- 11 company\-paid holidays
- Virtual team building activities, lunch and learns, and other company\-wide events!
- Offices in SF, NYC, London, Dublin, Tel Aviv, and Sydney
\#LI\-remote
*At Vanta, we are committed to hiring diverse talent of different backgrounds and as such, it is important to us to provide an inclusive work environment for all. We do not discriminate on the basis of race, gender identity, age, religion, sexual orientation, veteran or disability status, or any other protected class. As an equal opportunity employer, we encourage and welcome people of all backgrounds to apply.*
About Vanta
We started in 2018, in the wake of several high\-profile data breaches. Online security was only becoming more important, but we knew firsthand how hard it could be for fast\-growing companies to invest the time and manpower it takes to build a solid security foundation. Vanta was inspired by a vision to restore trust in internet businesses by enabling companies to improve and prove their security. From our early days automating security monitoring for compliance standards like SOC 2, HIPAA and ISO 27001 to creating the world's leading Trust Management Platform, our vision remains unchanged.
Now more than ever, making security continuous—not just a point\-in\-time check— is essential. Thousands of companies rely on Vanta to build, maintain and demonstrate their trust— all in a way that's real\-time and transparent.
Referral Instructions
If you are being referred for the role, please contact that person to apply on your behalf.
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 Vanta, 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.
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.
Vanta AI Hiring
Vanta has 3 open AI roles right now. They're hiring across AI Product Manager, Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $247K - $286K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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