Data Science Intern

02 · Requirements and your timeline

Check the actual eligibility requirements.

Degree levels recorded

bachelors · masters

Graduation window

No graduation window stated

Start date

Not confirmed

Deadline

No fixed deadline confirmed

Minimum years of experience

No numeric minimum confirmed

No fixed application closing date is published in the reviewed JD or job-specific ATS application metadata; apply promptly and reconfirm before submitting.

12-week Summer 2027 data science internship in New York. BA/BS or graduate degree in a quantitative discipline, or equivalent experience; SQL, Python, modeling and interest in A/B testing, causal inference or machine learning. No fixed graduation dates stated.

• Role-specific sponsorship and CPT/OPT support are not confirmed.
• A graduate degree is mentioned broadly; specific program eligibility should be confirmed.
• Coinbase limits candidates to three applications in six months.

Questions still to resolve

• Confirm sponsorship for the specific role and your circumstances.
• E-Verify enrollment has not been matched to the hiring legal entity. Verify separately if relevant to STEM OPT.
• Compare your degree and enrollment status with the official requirements.
• Confirm that applications are still being accepted.
• No fixed deadline was confirmed; check the official posting before applying.

Graduation dates are compared only where exact bounds are stated. We do not infer CPT/OPT eligibility, work permission or H-1B timing from your academic year.

03 · Separate employer check

E-Verify

Hiring legal entity not independently confirmed

No official E-Verify legal-employer match was verified for this posting. A general E-Verify notice is not a legal-entity match or proof of H-1B support.

E-Verify participation is separate from H-1B sponsorship and the other STEM OPT requirements. An employer statement or an unmatched name is not a completed legal-employer verification.

04 · Prepare with a purpose

What to prepare and why

Why prepare: the team explicitly applies experiments and models to product/business decisions. Preparation: produce a SQL/Python analysis, explain an experiment and demonstrate how you checked model quality and translated results into action.