Data science and machine learning roles, tracked live.
Graduate and internship data science, analytics and machine learning roles, synced daily from employers’ own careers systems, with what each loop actually tests.
37 open now · 1 opening soon · 38 tracked · synced 20 Oct 2026 · statuses recomputed from deadlines on every view
Open now (37)
Data Scientist, Analytics (University Grad) · Data & ML · New York, NY · Graduate · closes 2026-10-31
Deep Learning Intern · Data & ML · Santa Clara, CA · Internship · closes 2026-11-30
Data Analyst, Intern · Data & ML · Toronto · Internship
Data Analyst, Intern · Data & ML · New York, Seattle, South San Francisco HQ · Internship
Financial Data Analyst Intern, Technical Operations · Data & ML · Singapore · Internship
PhD Data Scientist, Intern · Data & ML · Toronto · Internship
PhD Data Scientist, Intern · Data & ML · New York, Seattle, South San Francisco HQ · Internship
Data Engineer Intern · Data & ML · Hybrid - San Francisco, CA · Internship
Data Science Intern · Data & ML · Hybrid - New York, NY · Internship
Data Engineer Intern (2027) · Data & ML · San Francisco, CA • New York, NY · Internship
Data Science Intern (2027) · Data & ML · San Francisco, CA • New York, NY · Internship
PhD Intern, Data Science (2027) · Data & ML · San Francisco, CA • New York, NY · Internship
Data Analyst Intern (Summer 2027) · Data & ML · New York, NY · Internship
Data Engineer Intern (Summer 2027) · Data & ML · Toronto, Canada · Internship
Data Science Intern, Algorithms (Summer 2027 - SF/NYC) · Data & ML · San Francisco, CA · Internship
Data Science Intern, Algorithms (Summer 2027 - SF/NYC) · Data & ML · New York, NY · Internship
Data Science Intern, Algorithms (Summer 2027 - Toronto) · Data & ML · Toronto, Canada · Internship
Master's Data Science Internship 2027 (USA) · Data & ML · San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; New York, NY, US · Internship
Master's University Grad Data Scientist (USA) · Data & ML · San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; New York, NY, US · Graduate
PhD Data Science Internship 2027 (USA) · Data & ML · San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; New York, NY, US · Internship
Campus Data Engineer (Intern) · Data & ML · Chicago · Internship
Campus Data Engineer (Intern) · Data & ML · London · Internship
2027 Technology Analyst AI and Data Science Graduate Apprenticeship Programme Glasgow · Data & ML · Glasgow Campus · Graduate · closes 2026-11-01
College to Corporate IT Internship - Data Science (NC) · Data & ML · Charlotte, NC · Internship
College to Corporate IT Internship - Data Science (PA) · Data & ML · Malvern, PA · Internship
College to Corporate IT Internship - Data Analyst (NC) · Data & ML · Charlotte, NC · Internship
2027 Summer Internship - MS/PhD Data Scientist, Proprietary Research · Data & ML · New York, NY · Internship
Data Engineer · Data & ML · New York, United States · Graduate
2027 - Internship, Data Engineering · Data & ML · Paris, London · Internship
2027 – Internship, Data Engineering · Data & ML · Hong Kong, Singapore · Internship
Working in Technology (WiT) Data Science - Summer 2027 · Data & ML · Chicago, Illinois, United States · Internship
Product Marketing, AI-Powered Data Engineering Intern (Spring 2027) · Data & ML · US-CA-Menlo Park · Internship
[2027] Data Scientist - PhD Intern · Data & ML · San Mateo, CA, United States · Internship
Data Science Intern · Data & ML · GB - London, United Kingdom · Internship
Data Science Graduate · Data & ML · PL - Warsaw, Poland · Graduate
Data Science Graduate · Data & ML · GB - London, United Kingdom · Graduate
AI Summer Intern – Data Science & AI · Data & ML · New York, NY · Internship · closes 2026-10-30
The opening calendar (1)
When applications for each are expected to open, soonest first. Where a firm has not published a date the row says so, and every row links to the firm’s own application page rather than asking you to take ours.
October 2026
How this route works
Two very different jobs share this vertical and most applications go to the wrong one. Product data science is an analytics and causal-inference role: SQL, experiment design, metric definition, and the judgement to say what a number means to a business decision. Machine learning engineering and research build and ship models, and are screened on modelling depth, coding and increasingly on the design of systems built around large models. The titles are used interchangeably by employers and are not interchangeable in the interview.
For the product data route the loop is unusually predictable. A SQL screen comes first at almost every employer, and it is a timed technical filter rather than a formality — window functions, multi-step aggregations and cohort logic against a schema you have not seen before. After that comes experimentation: designing an A/B test, sizing it, and reading a result honestly, including the cases where you should refuse to call it. Then a case round on metrics, usually framed as a number that moved and a question about why.
For the machine learning route the emphasis moves to modelling and to engineering judgement. Expect to justify a model choice against a simpler baseline, to talk credibly about evaluation and about what you would monitor after deployment, and — at an increasing number of employers — to discuss the design of a system built on top of a model rather than the model itself. Research seats generally expect publications or an equivalent track record; applied ML seats do not.
Finance employers on this board add a constraint the technology ones do not. Financial data is non-stationary, thin in the regimes that matter, and unforgiving of leakage, so a candidate who describes a validation approach that quietly uses the future is finished regardless of how good the model was. That is the single most common failure in these interviews and it is entirely preventable.
Frequently asked questions
What is the difference between a data scientist and a machine learning engineer?
A product data scientist answers business questions with data: SQL, experiment design, metric definition and causal inference, with the output being a decision someone else acts on. A machine learning engineer builds and ships models into production, and is screened on modelling depth, software engineering and evaluation. Employers use the two titles loosely, so read the responsibilities in the posting rather than the title, because the interview loops share very little.
What does a data science interview test?
For product roles: a timed SQL screen first, covering window functions, multi-step aggregations and cohort logic; then experiment design, including sizing a test and reading its result honestly; then a metrics case, usually "this number moved, tell me why". Statistics and probability appear throughout. For machine learning roles the weight shifts to model selection against a baseline, evaluation, and the engineering around deployment.
Do you need a PhD for a machine learning role?
For research positions at most large employers, effectively yes or an equivalent publication record. For applied machine learning and machine learning engineering, no — those hire strongly at bachelors and masters level and select on demonstrated ability to ship something that works, which a substantial personal project can evidence as well as a degree can.
What is different about data science interviews in finance?
The data. Financial series are non-stationary, so a model fitted on one regime often fails in the next, and the periods that matter most are the thinnest. Lookahead bias is the dominant failure: any validation scheme that lets information from the future reach the training set produces a backtest that looks excellent and is worthless. Expect to be asked how you would validate a time series, and expect the answer to be judged more harshly than the model choice itself.
Before you apply
A deadline is only useful if the application behind it is ready. The screening stages differ by programme type, so the kit does too.
SQL Cohort Drill (free)
Retention, cohorts and window functions against a real schema. Nearly every product data loop opens with a SQL screen.
A/B Test Designer (free)
Power, minimum detectable effect and the traps. The experimentation round is where product data candidates are separated.
Product Metrics Tree (free)
Decompose a metric to the lever that moves it, which is the shape of the "our number dropped, why" question.
Data science questions in finance
What changes when the data is financial: leakage, non-stationarity, and why backtests flatter.
Tracking roles is step one
Applying well is the rest of it. These are the ways to get prepared before the deadline.
CV Review by a Human
Written margin-note feedback on structure, impact bullets and ATS-readability. Reviewed by Suro, not an AI score.
$25 48h turnaround
Cover Letter Review by a Human
Line-by-line review of argument, tailoring and tone, with a rewritten opening as a worked example.
$50 48h turnaround
L3VLUP Pro
The subscription: personalised alerts, Apply Packs, every answer marked, full history and the whole research library.
$25 /month
Applying? Don’t walk in cold.
Free drills in the Labs, structured workflows in Skills, and the Data & ML prep guide.