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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 By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations.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
We are looking for a talented, enthusiastic and dedicated person to join Bill.com’s Risk Strategy team. This role will report to VP of Fraud Risk Strategy and will work closely with cross functional teams to formulate fraud and credit risk management strategies and controls for payments across different channels. This person will be responsible for building and maintaining the risk strategies and collaborate with cross functional teams to operationalize these risk strategies. This position requires a person who has experience with using data to inform insights, influence stakeholders on relative priorities, management, refining risk strategies and driving initiatives.
We’d love to chat if you have:
- 8 years of experience in risk management within the Fintech or financial services industries with an emphasis on fraud and credit risks and 3 \- 5 years in management
- Bachelor's degree; MS/MA/MBA degree preferred in Business, Economics, Finance, Analytics, Mathematics, or a related field
- Strong knowledge of the fundamentals of risk strategy such as fraud detection and prevention, balancing customer experience against rising fraud threats, using data to draw insights for risk mitigation
- Adept at SQL queries, reporting and presenting findings; Proficiency in Excel and other visualization tools
- Strong organization and time management skills and the ability to prioritize manage multiple projects at once
- Ability to collaborate effectively with Product Management, Engineering teams to convey the strategy and work through the roadmap prioritization, planning and implementation. Also collaborating effectively with Customer success and sales teams while dealing with escalations and effectively communicating the risk policies to the broader organization.
- Extensive knowledge and experience with defining, developing, and deploying risk management methodologies and models including data science and rule\-based and predictive fraud models
- Expertise in driving operational efficiencies and scale through ongoing policy and model optimization.
- Communicate and liaise effectively with the Compliance team and formulate the strategy for new product launches.
- Experience managing cross\-functional, multi\-stakeholder processes, communicating and influencing stakeholders at various levels across functional boundaries
- Strong analytical abilities. Familiarity with typical credit data, systems \& technologies is highly valuable
- Hands\-on experience leveraging AI/ML tools to strengthen fraud strategy, including using LLM\-based signals, anomaly detection, or generative AI to identify emerging fraud patterns and inform rule and model development
- Familiarity with AI\-assisted analytics platforms (e.g., Python\-based ML libraries, AutoML tools, or vendor AI solutions) to accelerate fraud pattern recognition, risk scoring, and strategy iteration at scale
- Demonstrated ability to evaluate and govern AI\-generated outputs within a fraud risk context — including assessing model drift, managing false positive/negative tradeoffs, and knowing when to override automated fraud decisions
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.
Our ranges for each role and job level are based on a variety of factors including candidate experience, expertise, and geographic location and may vary from the amounts listed below. The role is also eligible for a competitive benefits package that includes: medical, dental, vision, life and disability insurance, 401(k) retirement plan, flexible spending \& health savings account, paid holidays, paid time off, and other company benefits. The estimated salary ranges noted below roles in the specific geographic zones
Zone 1\- San Francisco Bay Area CA (includes HQ), New York City, Seattle, Los Angeles County
$190,400 \- $238,050 USD
Zone 2\- CA (Non San Francisco Bay Area and Los Angeles County), Austin TX, Massachusetts
$171,400 \- $214,200 USD
Zone 3 \-Utah (includes Utah office), Houston TX, Florida, North Carolina
$161,800 \- $202,300 USD
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 $161K-$238K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $161K to $238K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BILL AI Hiring
BILL has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US. Compensation range: $233K - $238K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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