Data Scientist, Optimization - Pricing at Lyft
New York, NY
$128,000–$160,000from the description
Jul 6, 2026
New York, NY
Jul 20, 2026
What this job asks for AI summary
This role sits within Lyft's Planned Pricing team, which sets price targets to balance short- and long-term marketplace outcomes. The work centers on building and refining mathematical models — spanning optimization, prediction, and machine learning — that feed directly into production pricing systems. It suits a quantitative researcher comfortable moving between rigorous algorithmic problem-solving and close collaboration with engineering, product, and business partners to ship and measure real-world solutions.
Senior level · New York-Newark-Jersey City, NY-NJ-PA · Full-time
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 320 people in the New York-Newark-Jersey City, NY-NJ-PA area plausibly meet what this posting asks for (data scientists). range 170–420
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $103,548–$176,968). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (5)
The role sits at the boundary between Data Science (15-2051) and Operations Research (15-2031): the title is 'Data Scientist' but the primary work is building optimization and mathematical models for a pricing system, which is the core of operations research. 15-2051 was chosen because the posting also emphasizes ML, inference, experimentation, and data analysis alongside the optimization work.
A Ph.D. in Operations Research or a quantitative field is listed as the first experience bullet, but the posting immediately qualifies it with 'or related work experience', so no formal degree is hard-gated.
The JD requires end-to-end data work including querying and aggregation, which implies SQL or a similar query language, but no specific query tool is named; SQL is inferred as the canonical skill for this requirement.
No total years of experience are stated; seniority is assessed as Senior based on the scope (production algorithm ownership, cross-functional leadership, coaching teammates, end-to-end experimentation) rather than a stated year requirement.
The role is hybrid (3 days/week in-office in NYC) — not fully remote. The 'up to 4 weeks/year work from anywhere' flexibility does not make it a remote role.
Read the full posting
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