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About This Role
Innovate with purpose
At BILL, we believe in empowering the businesses that drive our economy. By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations. And we don't stop there: we're creating the future of financial automation so businesses can spend more time on what matters.
Working here means you become part of a vision\-driven team that's ready to tackle challenges and build cutting\-edge solutions. We value purpose, drive, and curiosity—and we thrive in a fast\-paced, ever\-changing environment. Whether in one of our offices in San Jose, CA, Draper, UT, or in a remote\-eligible role, BILLders collaborate to deliver real impact for businesses that need more time in their busy weeks.
BILL builds high performing teams and we seek to hire the best talent for every role. We're committed to building a workplace that fosters inclusion and diverse perspectives, valuing each person's unique skills and experiences. We'd love to hear from you—you might be just what we're looking for, whether in this role or another.
✨ Let's give businesses more time for what matters.
Make your impact within a rapidly growing Fintech Company
BILL's Compliance team is a highly energetic, collaborative, motivated, and effective team focused on building a state of the art compliance program to support BILL's mission of making it simple to connect and do business. We are focused on designing, developing, and maintaining best\-in\-class technology solutions to meet our compliance obligations for our rapidly expanding portfolio of products, deliver results, and support BILL's overall company strategy.
We are looking for a highly motivated Data Scientist to join the BILL Compliance Data \& Analytics team and help us design, develop, and maintain best\-in\-class technology solutions to meet our compliance obligations for our rapidly expanding portfolio of products.
In this role, you will
- Support BILL's Compliance technology ecosystem for its internal and third party tools, accelerators, and data sources
- Analyze and expand data\-related requirements provided by internal and external stakeholders.
- Design and implement repeatable and scalable solutions for data\-related requirements.
- Create self\-service dashboards and analytics solutions for use by internal stakeholder teams.
- Extract, transform, and analyze data from BILL source systems databases and data lakes.
- Support Data \& Analytics team to Implement automation frameworks for solutions by leveraging data analytics solutions, business intelligence software, and custom programming.
- Reverse engineer existing dashboards and analytics and enhance them based on updated requirements
- Contribute to tuning and maintenance of Compliance systems and solutions
- Create and enhance documentation of existing Compliance systems and processes.
- Develop and maintain Compliance metrics on an ongoing basis
- Perform troubleshooting and deep\-dive analysis into problems caused across one or more Compliance systems, perform root cause analysis, and recommend and/or implement fixes
- Support Data \& Analytics team members in designing technology accelerators to improve effectiveness and efficiency across the BILL's Compliance ecosystem
We'd love to chat if you have:
Must Have:
- Proficiency in SQL, Python, and Excel
- Experience in risk / compliance related / Trust \& Safety systems within Payments or Financial Services.
- Strong analytical skills with the ability to collect, organize, analyze, and disseminate large amounts of data with attention to detail and accuracy
- Ability to work independently, in a fast paced environment
- Strong communication skills and attention to detail
- Experience with Business Intelligence Tools like Tableau or Power BI
Nice to Have:
- Good perspective on data science development cycle (problem definition, feature engineering, modeling \+ evaluation, deploy \+ monitor \+ iterate in production)
- Experience with the modern data stack (Fivetran / Starburst / dbt or similar)
- Familiarity with MLOps/infra required to support data science solutions
- Familiarity with prompt engineering and experience working with models like Sonnet and Gemini
- Familiarity with building out pipelines and use of Palantir Foundry, and/or backend development and relevant programming
- Compliance experience, specifically anti\-money laundering
Visa Sponsorship: Please note that this position is not eligible for visa sponsorship. Applicants must have authorization to work in the United States without requiring visa sponsorship now or in the future.
What's in it for you?
Redefining how businesses automate their work is a fast\-paced, exciting, and fun environment. But we also have benefits and perks to ensure the magic isn't only experienced by our customers, but by our employees as well.
Here is a preview of some of the amazing benefits here at BILL:
- 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP)
- HSA \& FSA accounts
- Life Insurance, Long \& Short\-term disability coverage
- Employee Assistance Program (EAP)
- 11\+ Observed holidays and wellness days and flexible time off
- Employee Stock Purchase Program with employee discounts
- Wellness \& Fitness initiatives
- Employee recognition and referral programs
- And much more
Don't believe us? Check out our culture, benefits, and teams on our career site, LinkedIn Life, or YouTube pages.
BILL is an Equal Opportunity Employer. We believe our best ideas come from the unique stories, perspectives, and experiences of our team members. We welcome people of all backgrounds, abilities, and identities to bring their authentic selves and contribute to our culture.
We are committed to a transparent, inclusive hiring process that reflects our values. If you need accommodations at any stage, please contact [email protected]. To ensure a fair evaluation, our Candidate Integrity Policy prohibits the use of unapproved external assistance, including generative AI, during live interviews or assessments. Doing so will result in a review and potential disqualification.
Our Applicant Privacy Notice describes how BILL treats the personal information it receives from applicants.
Salary Context
This $112K-$135K range is in the lower quartile 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 BILL, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($123K) sits 36% below the category median. Disclosed range: $112K to $135K.
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.
BILL AI Hiring
BILL has 2 open AI roles right now. They're hiring across Data Scientist. Based in San Jose, CA, US. Compensation range: $135K - $135K.
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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