The fintech companies with the most data science and machine learning vacancies right now are Stripe, Coinbase, Affirm, Monzo and Plaid. As of 1 October 2026, Finjobsly tracked 186 live data, analytics and machine learning roles across 26 of the 33 employers on its panel, about 6.9% of the 2,692 active roles it follows. Stripe alone accounts for 36 of them and Coinbase for 24. The roles skew senior and remote, and the most requested skills are SQL and Python. This guide shows who is hiring, which role types they are filling, what they pay where pay is disclosed, and how to read the numbers.
Key findings
- Finjobsly tracked 186 live data and machine learning roles at 26 employers as of 1 October 2026, out of 2,692 active roles across 33 employers.
- The five largest employers by data role count are Stripe (36), Coinbase (24), Affirm (14), Monzo (13) and Plaid (13). Stripe and Coinbase together hold 60 of the 186 roles, so the market is concentrated.
- Senior is the most common level: 88 of the 186 roles are tagged Senior, 28 Staff and 14 Lead, against 36 Mid-Level, 11 internships and 7 Junior.
- Of the 133 roles with a work style tagged, 100 are remote (75%), 24 hybrid and 9 onsite.
- SQL and Python each appear in the skill tags of 132 of the 186 roles. Machine Learning appears in 80, Payments in 69 and Fraud in 50.
- Among USD-paid roles that disclose a range, median advertised pay is highest for machine learning roles (a median range of $215,013 to $315,360, n=16) and lowest for risk and fraud analytics ($125,000 to $160,000, n=9).
Which fintech companies are hiring the most data scientists?
Hiring volume is a measurable signal, not a quality ranking. The table counts live roles on Finjobsly whose titles match data science, machine learning, data or analytics engineering, data or business analytics, or risk and fraud modelling, as of 1 October 2026. It covers Finjobsly's panel of 33 tracked employers, not every fintech.
| Employer | Data and ML roles | Data scientist | Machine learning | Data and analytics engineer | Analyst | Risk and fraud analytics |
|---|---|---|---|---|---|---|
| Stripe | 36 | 9 | 9 | 1 | 10 | 7 |
| Coinbase | 24 | 4 | 7 | 5 | 3 | 5 |
| Affirm | 14 | 0 | 4 | 2 | 7 | 1 |
| Monzo | 13 | 7 | 5 | 1 | 0 | 0 |
| Plaid | 13 | 5 | 5 | 1 | 0 | 2 |
| Robinhood | 11 | 5 | 2 | 1 | 2 | 1 |
| Block | 11 | 4 | 3 | 2 | 2 | 0 |
| Adyen | 7 | 0 | 1 | 0 | 1 | 5 |
| Ripple | 7 | 3 | 0 | 0 | 4 | 0 |
| Toast | 6 | 1 | 0 | 1 | 2 | 2 |
| SoFi | 6 | 1 | 1 | 0 | 3 | 1 |
| GoCardless | 6 | 1 | 0 | 2 | 3 | 0 |
Gusto and Chime each had 5 roles, Brex, Kraken and Gemini 3 each, and a further nine employers had one or two. Stripe's 36 roles are 19% of all data roles in the panel, which means any "share of the market" figure in this article is partly a statement about Stripe. Rows are counted by title, so a role can only sit in one column.
Two patterns stand out. Monzo, Plaid and Robinhood lean towards data scientist and machine learning titles. Adyen's data hiring is mostly risk and fraud analytics (5 of 7). You can browse every live role through the data scientist job search and the fintech company directory.
What types of data jobs are fintech companies recruiting for?
Finjobsly's 186 roles split across five families, based on job titles:
- Data and business analysts: 49 roles at 19 employers. This is the broadest family and the most common entry point, and 20 of these roles disclose a USD range.
- Data scientists: 45 roles at 15 employers, including product, fraud, lending and risk-focused scientists.
- Machine learning engineers and scientists: 42 roles at 13 employers, covering fraud models, ML platforms, identity and customer experience.
- Risk and fraud analytics: 28 roles at 12 employers, including credit model risk and financial crime analytics.
- Data and analytics engineers: 22 roles at 15 employers, building the pipelines and market data that the other teams rely on.
Because the families are assigned by title keywords, borderline titles such as "software engineer, ML platform" are classed with machine learning and some product analyst roles may sit in the analyst family. Treat the splits as a guide, not an official taxonomy.
What seniority level are fintech data science jobs?
Senior and above dominates. Of the 186 roles, 88 are tagged Senior, 28 Staff and 14 Lead (130 in total), against 36 Mid-Level, 11 internships and 7 Junior roles. Two roles had no level tagged. Candidates earlier in their careers will find far fewer openings, and most of those sit in the analyst family and in internships. If you are a junior candidate, the fintech data analyst salary guide is a better starting point than a data scientist search.
Are fintech data science jobs remote?
Mostly, among those that state it. Work style is tagged on 133 of the 186 roles: 100 are remote (75.2%), 24 hybrid (18.0%) and 9 onsite (6.8%). The other 53 roles have no work style recorded, so the true remote share may be different. Note that "remote" in a job posting often still carries a country or time zone restriction, so check the posting.
What skills do fintech data science jobs ask for?
These counts come from the skills tags on the same 186 roles, as of 1 October 2026. A role can carry several tags.
- SQL: 132 roles (71%)
- Python: 132 roles (71%)
- Data Science: 87 roles
- Machine Learning: 80 roles
- Payments: 69 roles
- Fraud: 50 roles
- Compliance: 40 roles
- Go: 31 roles
- Risk Management: 28 roles
- Tableau: 27 roles
- Data Engineering: 27 roles
- AWS: 20 roles
The domain tags matter as much as the tools. Payments (69), Fraud (50) and Compliance (40) show up on a large share of data roles, which supports a point many hiring managers make: knowing how money moves and which controls apply is part of the job. SQL and Python are close to universal, so they are the baseline rather than a differentiator. Because Stripe and Coinbase supply a third of the roles, some of these tags reflect their stacks and products.
How much do fintech data scientists earn?
Finjobsly's figures use only roles that advertise a salary range in US dollars. 99 of the 186 roles disclose some pay, and 69 of them are USD-denominated. The table shows the median of the advertised minimum and the median of the advertised maximum for each family. Sample sizes are small, and anything under 20 should be read as indicative.
| Role family | Median advertised minimum (USD) | Median advertised maximum (USD) | Roles (n) |
|---|---|---|---|
| Machine learning | $215,013 | $315,360 | 16 |
| Data scientist | $190,800 | $245,916 | 15 |
| Data and analytics engineer | $180,370 | $212,200 | 9 |
| Data and business analyst | $149,500 | $200,000 | 20 |
| Risk and fraud analytics | $125,000 | $160,000 | 9 |
These are advertised ranges at the employers in Finjobsly's panel, concentrated in US tech-led fintechs where pay disclosure is common or legally required. They are not market averages and they exclude roles paid in other currencies. For context, the US Bureau of Labor Statistics reports a median annual wage of $120,230 for data scientists across all industries as of May 2025 and projects employment growth of 35% from 2025 to 2035 (US Bureau of Labor Statistics, Occupational Outlook Handbook, accessed 1 October 2026). Advertised fintech ranges at the senior end sit well above that all-industry median. To check a specific offer, use the Finjobsly Salary Benchmark.
Which fintech companies are worth considering for a data science career?
There is not enough comparable evidence, such as retention, promotion or model-quality data, to rank employers numerically, so this is a shortlist based on observable hiring signals rather than a league table.
- Breadth of data roles: Stripe and Coinbase are hiring in all five families, which suggests larger and more specialised data organisations.
- Machine learning and data science depth: Monzo (12 of 13 roles are data scientist or machine learning titles) and Plaid (10 of 13) concentrate on modelling.
- Risk and fraud specialisation: Adyen lists 5 risk and fraud analytics roles among its 7, and Stripe lists 7.
- Disclosed pay: Plaid, Block, Coinbase and Stripe appear repeatedly among the highest advertised USD ranges in the panel, which makes their roles easier to compare.
Hiring volume says nothing about culture or management quality. Read the individual job posts and company pages before applying.
Current opportunities
Browse live roles by data scientist, machine learning, data engineer and data analyst, or see the wider fintech jobs inventory. For a view of how this fits the wider market, read which fintechs are hiring for AI roles and the Finjobsly Fintech Hiring Index.
Methodology
All Finjobsly figures come from the platform's live job database, as of 1 October 2026, covering 2,692 active roles across 33 tracked employers. This panel is not the whole fintech market, so the figures describe roles Finjobsly tracks. A role counts as data, analytics or machine learning if its title matches data scientist, machine learning, ML, MLOps, applied or research scientist, data engineer, analytics engineer, data analyst, business intelligence, analytics, or a fraud, risk or credit analytics or modelling title. Skills counts use each role's skill tags. Work style, seniority and pay are shown only where recorded. Work style is missing on 53 roles. Country data is not consistently recorded, so this article does not report location splits. Salary figures cover USD-paid roles only, and per-family sample sizes are small. External figures are from the US Bureau of Labor Statistics.
Last updated: 1 October 2026.
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