The Data Scientist List
Live indexThe best data scientist jobs, ranked.
Data science pay has a wide spread: mid-level bands at the big product companies post between roughly $135k and $210k before bonus and equity, OpenAI posts $230k–$385k, and Netflix $280k–$421k in cash. This is the living index of the data science orgs worth your application — posted pay, which companies run a real ladder or an entry path, and whether they hire remote, re-verified as the market moves. Every number traces to a posted range, a live careers page, or levels.fyi.
Sources: posted salary ranges, levels.fyi, and company careers pages. Last verified October 11, 2026. How we rank →
Google data scientists apply statistics, experimentation and increasingly ML to improve products, measure ads and search impact, and guide business decisions across three tracks. Hiring is rolling per req over about six to eight weeks, and interns follow a separate fall cycle.
Salary band, hiring status & score breakdown — see the full Google Data Scientist page →Google interview guide →
Meta data scientists are embedded product-analytics partners who define metrics, run experiments and shape product strategy; the work is analytics-heavy rather than model building. Hiring is rolling and city-based, and runs about two to three months end to end.
Salary band, hiring status & score breakdown — see the full Meta Data Scientist page →Meta interview guide →
OpenAI data scientists embed with product, go-to-market, safety and finance teams to define metrics, run A/B tests and build dashboards for ChatGPT, Codex, the API and ads. Hiring is rolling through Ashby and skews senior, with a take-home challenge at the front of the loop.
Salary band, hiring status & score breakdown — see the full OpenAI Data Scientist page →OpenAI interview guide →
Netflix data scientists are embedded partners to product and business teams, centered on A/B testing and causal inference; model building sits in a separate ML Scientist track. Hiring is rolling, experienced-only and fully remote in the US, with salary-only pay.
Salary band, hiring status & score breakdown — see the full Netflix Data Scientist page →Netflix interview guide →
Amazon data scientists build econometric, causal, forecasting and ML models for operations, devices, Prime Video and AWS decisions. The role leans toward inference and experimentation with ML, sitting between Business Intelligence Engineer (SQL and reporting) and Applied Scientist (production ML), and hiring is rolling, team by team.
Salary band, hiring status & score breakdown — see the full Amazon Data Scientist page →Amazon interview guide →
Apple data scientists work inside product, services, ML and corporate teams on fraud and risk, ads and Apple Pay measurement, AI and ML model evaluation and decision intelligence. The mix runs from analytics and product-facing (IS&T, Strategic Data Solutions) to ML-evaluation-heavy (AIML), and hiring is rolling, team by team.
Salary band, hiring status & score breakdown — see the full Apple Data Scientist page →Apple interview guide →
Pinterest data scientists partner with engineering, product, ads and marketing on metrics, experimentation and measurement: the Infrastructure DS II req is measurement and data-foundations work, and the grad req is product analytics with A/B testing. The work is analytics and inference first, with forecasting on some Staff reqs; experienced hiring is rolling and university hiring is seasonal.
Salary band, hiring status & score breakdown — see the full Pinterest Data Scientist page →Pinterest interview guide →
Uber scientists solve marketplace problems with ML, optimization, causal inference and experiments across delivery, maps, membership and pricing. The title is ‘Scientist’ rather than ‘Data Scientist’, hiring is rolling through Uber’s careers site, and the open reqs skew Senior and Staff.
Salary band, hiring status & score breakdown — see the full Uber Data Scientist page →Uber interview guide →
Airbnb data scientists work on growth, pricing, marketplace ranking and AI-driven support using causal inference, experimentation and ML. Hiring is rolling through Greenhouse; volume is small and skews Staff, and every open req is remote-eligible.
Salary band, hiring status & score breakdown — see the full Airbnb Data Scientist page →
Microsoft data scientists sit in the Data Science discipline inside product and business teams such as M365 Copilot, marketing analytics and customer engineering. The posted DS II role is ML-heavy (LLM evaluation, RAG, experimentation), and mid-level hiring is thin, with most open reqs at Senior and Principal.
Salary band, hiring status & score breakdown — see the full Microsoft Data Scientist page →Microsoft interview guide →
DoorDash data scientists sit in the central Analytics org and do product and business analytics: experiments, funnel and cohort work, regressions and dashboards for the marketplace, growth, logistics and merchant teams. The work is analytics and inference rather than ML, and hiring runs through one evergreen req with team matching after the interviews.
Salary band, hiring status & score breakdown — see the full DoorDash Data Scientist page →DoorDash interview guide →
Spotify’s US data scientists work in Product Insights for the Music Mission (Discovery Mode, Marquee, Showcase): metrics, dashboards and experiments, with ML model evaluation on the newer req. It is product analytics and experimentation, some of it ML-adjacent, hired one req per team on a rolling basis.
Salary band, hiring status & score breakdown — see the full Spotify Data Scientist page →Spotify interview guide →
The comp landscape
Posted base-salary bands for each program’s core mid-level data scientist req, US market — equity and bonus come on top, and at the biggest programs they can add half again. Bands span levels; senior hires land near the top. Full breakdown in the Data Scientist salary guide.
don’t post salary ranges and are omitted here.
What does a data scientist do?
A data scientist turns product and business questions into measurable answers: defining the metrics a team runs on, designing and reading experiments, building models that forecast demand or rank what users see, and telling product and leadership what to do next. At the companies on this list the role sits inside the product organization rather than in a central reporting team, and it leans on SQL, Python or R, statistics and product judgment more than on building pipelines (data engineering) or production ML systems (ML engineering).
Titles and tracks vary: Meta runs the largest product-analytics data science ladder in tech, Airbnb splits the role into analytics, inference and algorithms tracks, Amazon separates data scientists from applied scientists, and Netflix leans on analytics engineers alongside data scientists. The interview loop is consistent: SQL (usually live), statistics and probability, a product-sense or metrics case, an experimentation or A/B-testing round, and behavioral.
Preparing for the loop? Start with our guides to the Meta data scientist interview, Google data scientist interview, and Netflix data scientist interview.
How we rank
Every program gets an Index score out of 100, built from four weighted dimensions. Scores are editorial judgments grounded in the data below — not paid placements.
Posted data scientist salary bands, levels.fyi data, and verified offer reports — total comp where a data-scientist-specific sample exists, base bands otherwise.
How real the data science org is: a dedicated data scientist ladder, named specialties (product analytics, inference, algorithms), experimentation platforms, and whether there is an intern or new-grad path.
What the role does for the rest of your career — brand strength, the scale of the product and experiments you work on, and how close data scientists sit to product decisions.
Open req counts, remote and entry-level paths, and whether the data science org is expanding, holding, or working through a restructuring.
We also track LinkedIn, Instacart, Capital One, Stripe, Lyft, Snap, Roblox and Walmart, currently outside the ranking and queued for the next data pass. Uber stays ranked without a ‘Data Scientist’ title because its Scientist ladder does the same work. Total compensation is shown only where levels.fyi has a data-scientist-specific sample at the mid level, which is why Netflix, OpenAI, Airbnb and Spotify show base bands alone.
Compensation data comes from posted salary ranges (required in several US states), levels.fyi, and public offer reports; figures are US-market and vary by level and location. Hiring status reflects each company’s careers page as of October 11, 2026. Spotted something outdated? Email [email protected].
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