DoorDash

DoorDash

Manager, Data Science

US · Senior

Sponsorship not specified$184k-$271kDetected 6 days ago
Data ScienceStatisticsA/B TestingLogistics

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odds of building a lasting career here

64Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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Employer immigration record

from this employer's Department of Labor filings

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 22 green-card filings in this occupation, 100% were for a worker who already held the job.

Green-card follow-through: 53%

Of 34 labor certifications old enough to have been used, 16 expired without the employer filing the next step. Median time from filing to decision: 481 days.100% 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.

Files H-1B transfers

125 transfer filings in the last year, covering 125 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

  • Merchant Analytics helps DoorDash make better product, business, and go-to-market decisions through high-quality analytics, predictive modeling, experimentation, and strategic thought partnership.
  • You will report into Director, Data Science on our Merchant Analytics team in our Analytics organization.
  • You're excited about this opportunity because you will…

Responsibilities

  • Lead and develop a team of data scientists responsible for high-impact analytics, predictive modeling, and decision support tied to DoorDash's most important product, business, and GTM priorities.
  • Build a high-performing pod that balances analytical rigor, strong prioritization, and clear storytelling in a fast-moving environment.

Requirements

  • You have a degree in Math, Physics, Statistics, Economics, Computer Science, or a similar domain
  • You have 6+ years of experience in analytics, data science, or a related quantitative field, including experience leading complex, ambiguous work from problem framing through recommendation.

Skills

  • Scale reusable analytics frameworks, tools, models, and best practices that make the broader organization more effective over time.

Compensation

  • The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.
  • Base salary is localized according to an employee's work location.
  • In addition to base salary, the compensation for this role includes opportunities for equity grants.
  • flexible paid time off/vacation, plus 80 hours of paid sick time per year.
  • For hourly roles:

Benefits

  • In addition to base salary, the compensation for this role includes opportunities for equity grants.
  • DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.
  • To learn more about our benefits, visit our careers page here.
  • See below for paid time off details:

Company info

  • We work across some of DoorDash's most important merchant and marketplace priorities, building the measurement, insights, and decision frameworks that improve outcomes for merchants and drive company impact.

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