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About This Role
At Ripple, we're building a world where value moves like information does today. It's big, it's bold, and we're already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you're ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
At Ripple, we're building a world where value moves like information does today. It's big, it's bold, and we're already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you're ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
The work:
We're looking for a Senior Data Scientist to drive analytics across Ripple's product and business portfolio. You'll build the scientific frameworks teams use to evaluate product and business performance, and use AI tooling to accelerate the speed and reach of your analysis.
In this role, you'll partner with product and business leads to frame the right questions, bring rigor to how they're answered, and make sure decisions rest on a consistent, thorough foundation. You'll take on ambiguous, high-impact problems, build analyses and tooling others can reuse, and raise the analytical bar of the teams you work with.
What you'll do:
- Serve as the data science lead for one or more product or business areas — Payments, Stablecoin, or Custody — applying strong methodology and tackling the hardest analytical problems in your domain.
- Partner with product and business leads to shape roadmap decisions: which initiatives to prioritize, what success looks like, and how we'll measure it.
- Build the scientific frameworks your teams rely on: product and network health metrics, causal inference approaches, and forecasting that holds up across institutional and developer surfaces.
- Apply AI to accelerate analytics — using LLMs and agentic workflows to scale insight generation, automate routine analysis, and make data more self-serve for non-DS partners.
- Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, surfacing the causal drivers behind adoption and volume.
- Define and communicate the metrics your teams and leadership run on, translating complex results into clear narratives for senior stakeholders.
- Raise the bar for the DS function through mentorship and by modeling strong analytical practice for the data scientists around you.
What you'll bring:
- 7+ years in data science or quantitative analysis, with a track record of measurable impact on product and business decisions.
- Experience as the data science partner to cross-functional teams, shaping roadmaps and helping leaders make better-informed calls.
- Track record designing analytics and measurement frameworks that other teams adopt and build on.
- Hands-on experience applying AI to accelerate analytics workflows — agentic analysis, AI-assisted insight generation, natural-language data interfaces.
- Strong expertise in experimentation, causal inference, forecasting, and statistical modeling.
- Expertise in Python or R, fluency in SQL, and experience with large-scale data tech (Databricks).
- Experience with FinTech, payments, crypto, or blockchain data is a strong plus.
- Advanced degree (MS, PhD) in a quantitative field preferred.
- Excellent communication skills — able to translate technical depth into clear narratives for senior stakeholders.
WHO WE ARE:
Do Your Best Work
- The opportunity to build in a fast-paced start-up environment with experienced industry leaders
- A learning environment where you can dive deep into the latest technologies and make an impact. A professional development budget to support other modes of learning.
- Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify with, every employee is a respected, valued, and empowered part of the team.
- In-office collaboration for moments that matter is important to our culture, and we give managers and teams the flexibility to decide which 10+ days a month they come in.
- Bi-weekly all-company meeting - business updates and ask me anything style discussion with our Leadership Team
- We come together for moments that matter which include team offsites, team bonding activities, happy hours and more!
Take Control of Your Finances
- Competitive salary, bonuses, and equity
- Competitive benefits that cover physical and mental healthcare, retirement, family forming, and family support
- Employee giving match
- Mobile phone stipend
Take Care of Yourself
- R&R days so you can rest and recharge
- Generous wellness reimbursement and weekly onsite & virtual programming
- Generous vacation policy - work with your manager to take time off when you need it
- Industry-leading parental leave policies. Family planning benefits.
- Catered lunches, fully-stocked kitchens with premium snacks/beverages, and plenty of fun events
Benefits listed above are for full-time employees.
Ripple is an Equal Opportunity Employer. We're committed to building a diverse and inclusive team. We do not discriminate against qualified employees or applicants because of race, color, religion, gender identity, sex, sexual identity, pregnancy, national origin, ancestry, citizenship, age, marital status, physical disability, mental disability, medical condition, military status, or any other characteristic protected by local law or ordinance.
Please find our UK/EU Applicant Privacy Notice and our California Applicant Privacy Notice for reference.
Salary Context
This $164K-$205K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Ripple, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $164K to $205K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Ripple AI Hiring
Ripple has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $205K - $250K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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