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About This Role
About Gusto
At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.
All full\-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.
AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.
About the Role:
Gusto is looking for an experienced Senior Data Science Leader to empower our Sales Data team, the team that powers the insights, forecasting, and measurement infrastructure behind how we acquire, expand, and retain our revenue base.
We expect this leader to be both a strategic partner and execution\-focused. You'll guide senior sales and marketing leaders through complex analytical questions, develop analysts into statistical and scientific thinkers, and collaborate cross\-functionally with stakeholders across Sales Operations, Data Engineering, Finance, R\&D, and more.
As AI tooling becomes deeply embedded in Gusto's data infrastructure, the nature of analytics work is fundamentally shifting. Tasks that once consumed a significant portion of our analyst's time, such as reporting, dashboard maintenance, and answering ad hoc questions, are increasingly handled by a maturing self\-serve ecosystem. What remains are the hard problems: ones that require rigorous statistical thinking, causal reasoning, and the ability to draw defensible conclusions in messy, real\-world conditions where controlled experiments aren't always possible.
This role has two equally important mandates. The first is operational: Gusto's sales data foundation has significant technical debt, and this leader will need to partner closely with Data Engineering to assess the current state, architect a modern and scalable data layer, and execute a phased remediation. The second is transformational: as the foundation gets rebuilt, this leader must simultaneously evolve what the team does with it: moving from a function defined by query fulfillment and reactive reporting toward one defined by advanced forecasting capabilities, causal inference, and analytical rigor. The ideal candidate brings deep expertise in Sales data, a commitment to building strong foundations for scale, paired with a genuine command of experimentation and quasi\-experimental methods, and can instill these capabilities across the team.
Here's what you'll do day\-to\-day:
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- Lead the Sales Analytics team \- Drive vision for and champion a team of data scientists and analysts to deliver impact across customer acquisition and product expansion sales teams.
- Insights and Recommendations: Conduct deep\-dive analyses to identify revenue drivers and anomalies. Translate complex data into clear, actionable insights and recommendations for senior leadership and cross\-functional partners.
- Rebuild the data foundation \- alongside Data Engineering and Service Platform partners, drive the evolution of our foundational data systems needed to build a world class sales analytics ecosystem.
- Partner with key stakeholders \- work closely with the Chief Revenue Officer, Sales Operations, Data Engineering, and the Service Platform team to align on a shared data and infrastructure roadmap.
- Evolve forecasting capabilities \- Evolve our sales forecasting methodology to incorporate more rigorous analytics methods (eg propensity scoring, better seasonality controls, etc)
- Enable self\-service analytics with AI \- leverage Gusto's move toward AI\-first development to create self\-service analytics capabilities for operations partners. Stay ahead of emerging AI tools that can drive efficiency and accuracy in revenue analytics.
- Drive team excellence \- Recruit, mentor, and develop a high\-performing, proactive analytics team, shifting the culture from reactive query fulfillment toward strategic analysis. As AI handles more of the routine reporting, define what excellence looks like for analysts in a world where causal thinking and statistical fluency are the new baseline.
- Act as connective tissue across the data org \- identify opportunities where deeper technical solutions (e.g., from Data Engineering, Analytics Engineering, Data Science, or AI/ML Engineering) could accelerate revenue\-driving analytics, and proactively bring the right partners into the conversation.
What we're looking for:
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- 10\+ years of experience in data science or related fields, with at least 4\+ years leading a growing analytics team.
- Leadership with a Builder Mindset: A dynamic leader who inspires and develops teams while maintaining a "roll up your sleeves" attitude — able to step into the details when needed to build reports, run analyses, and troubleshoot.
- Statistical and causal inference expertise \- strong command of experimental design, causal reasoning, and quasi\-experimental methods (e.g., difference\-in\-differences, synthetic control, regression discontinuity, propensity score matching) for settings where A/B testing isn't possible. Comfortable navigating the assumptions required to make credible causal claims from observational data, and able to communicate those tradeoffs clearly to non\-technical stakeholders.
- Revenue data systems expertise \- deep experience working with Salesforce data at scale, including understanding data extraction strategies, CRM\-to\-warehouse reconciliation, and the challenges of treating Salesforce as a source of truth.
- Infrastructure\-first mindset \- proven track record of inheriting messy, tech\-debt\-laden data environments and rebuilding foundations with long\-term scalability in mind. Thinks in terms of systems, not patches.
- Technical depth \- strong SQL skills, hands\-on experience with dbt and Snowflake, comfort navigating transformation logic across multiple layers (Salesforce, BI, dbt, dashboards), and ability to mentor analysts on best practices.
- Sales and revenue domain knowledge \- strong understanding of pipeline management, forecasting, quota and attainment tracking, acquisition and expansion motions, and cross\-sell/upsell analytics in a multi\-product SaaS environment.
- Cross\-functional partnership \- ability to negotiate priorities and drive shared roadmaps with platform engineering, data engineering, and sales operations teams. Can go toe\-to\-toe with service platform managers on technical trade\-offs.
- Change management and team development \- experience leading teams through significant transformation, raising performance expectations, coaching analysts toward more strategic work, and making tough talent decisions when needed.
- Strong communication skills \- experience presenting infrastructure roadmaps and analytical insights to executive stakeholders, translating complex data system challenges into clear business terms.
- AI\-forward orientation \- has a clear\-eyed view of how AI tooling is reshaping the analyst role. Understands that as self\-serve and automation absorb routine reporting and ad hoc work, the value of the team increasingly lives in statistical rigor, causal thinking, and the ability to answer questions that can't be solved with a dashboard. Can articulate that vision compellingly and recruit, develop, and retain talent accordingly.
- SaaS/Growth Background: Proven track record of successfully building and leading analytics functions in a high\-growth SaaS or technology environment.
Our annual base salary compensation range for this role is $218,000 \- $255,000 in San Francisco \& New York, and $185,000 \- $217,000 in Denver. Final offer amounts are determined by multiple factors, including candidate experience and expertise, and may vary from the amounts listed above.
Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees who are based in those locations will be expected to work from the office on designated days approximately 2\-3 days per week (or more depending on role). The same office expectations apply to all Symmetry roles, Gusto's subsidiary, whose physical office is in Scottsdale.
Note: The San Francisco office expectations encompass both the San Francisco and San Jose metro areas.
When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. This includes non\-office days for hybrid employees.
Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home at Gusto.
Gusto is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Gusto considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Gusto is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. We want to see our candidates perform to the best of their ability. If you require a medical or religious accommodation at any time throughout your candidate journey, please fill out this form and a member of our team will get in touch with you.
Gusto takes security and protection of your personal information very seriously. Please review our Fraudulent Activity Disclaimer.
Personal information collected and processed as part of your Gusto application will be subject to Gusto's Applicant Privacy Notice.
Salary Context
This $185K-$255K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gusto, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $185K to $255K.
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.
Gusto AI Hiring
Gusto has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $255K - $255K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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