Duolingo

Duolingo

Data Scientist, Intern (PhD)

New York, NY; Pittsburgh, PA · Intern · Internship

Sponsorship not specifiedDetected 5 days ago
PythonSpringSQLBigQuerySnowflakeMachine LearningdbtData ScienceStatistics

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odds of building a lasting career here

42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

Lottery odds assume a STEM candidate.

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Employer immigration record

from this employer's Department of Labor filings

Green-card follow-through: 89%

Of 9 labor certifications old enough to have been used, 1 expired without the employer filing the next step. Median time from filing to decision: 488 days.93% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

DOL often reclassifies their job titles

The Department of Labor assigned a different occupation than this employer requested on 45% of its 20 wage determinations — against a 18% norm. The occupation sets the prevailing wage floor.Reclassification is routine and can reflect genuinely hybrid roles; it is not by itself evidence of underpayment.

Files H-1B transfers

21 transfer filings in the last year, covering 21 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • Communicate and present findings and recommendations to senior management
  • Help preserve our culture of fact-based decision making as we scale
  • Previous experience analyzing large datasets, communicating results to technical and non-technical senior-level audiences, and making actionable recommendations that improve the product or business

Responsibilities

  • Derive insights from data to influence product roadmaps and drive business results

Requirements

  • Experience with "big data" and cloud computing technologies like BigQuery, Snowflake, and/or dbt
  • Familiarity with AI tools such as Cursor, a knack for prompt engineering, and a desire to continue to innovate and learn in AI

Benefits

  • Take a peek at how we care for our employees' holistic well-being with our benefits here.
  • Our mission at Duolingo is to develop the best education in the world and make it universally available.
  • You'll have limitless learning opportunities and daily collaborations with world-class minds - while doing work that's both meaningful and fun.
  • Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world.

Company info

  • Enthusiasm for learning and sharing analytical methods to help grow a collaborative Data Science team and data-forward company culture
  • Frequent company-wide dinners, monthly celebrations, social clubs, and so much more!

Equal opportunity

  • Duolingo is proud to be an Equal Employment Opportunity employer.
  • We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
  • Fraud Warning: Unfortunately, there is a rise in scammers pretending to be real Duolingo employees.
  • Duolingo and our employees will never ask for your Social Security number, bank details, or passport info, and we'll never ask you to deposit a check, purchase equipment, or exchange money during the interview process.

This listing is sourced directly from Duolingo's careers page and normalized into a canonical job model.