$118.3k - $177.45k
advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, we’re making it easier for buyers and... ....Job SummaryFreewheel is currently looking to recruit a Data Scientist to…
Engineering & IT · New York, NY
Data Scientist jobs in New York, NY
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
3,390
Open roles today
$51–$127/hr
Typical pay range
$172k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 3,390 · sorted by freshness
$70 - $78 per hour
Snowflake Data Engineer (HVR REQUIRED) - U.S. Citizens OnlyU.S. CITIZENS ONLYNO SPONSORSHIP NOW OR IN THE FUTURENO H1BNO OPTNO CPTNO C2CNO THIRD-PARTY CANDIDATESMUST HAVE - READ BEFORE APPLYINGThis is NOT a general Snowf…
$164.6k - $213.1k
Grade 13T Posted Date 28-Sep-2026 Job ID 20769 Description and Requirements The Team You Will JoinAt MetLife, data isn’t just a tool - it is a catalyst for growth. As part of our Data & Analytics organization, you’ll unl…
TimeWorking Type On SiteJob Reference 0000017586Salary Type AnnuallyIndustry Hedge Fund;Private EquitySelling Points Drive impactful data engineering initiatives in a fast-paced financial environment. Collaborate on inno…
$100k - $150k
$100,000-$150,000 per annum New York, United States Permanent Data Catalog EngineerNew York, NY - Hybrid (3 Days Per Week in Office) My client is seeking a Data Catalog Engineer to join a growing Data Governance team foc…
TimeWorking Type HybridJob Reference 0000021410Salary Type AnnuallyIndustry Broker Dealer;Financial ServicesSelling Points Lead impactful data engineering projects at a leading organization. Drive innovation with modern…
$165k - $250k
Data ScientistWe're looking for a hard-working, thoughtful Data Scientist who delivers results. You'll transform messy data into useful models and decision-making tools, collaborate closely with engineering and business…
$138k - $197k
Make decisions from data, and be capable of maximizing impact by prioritizing requests from different stakeholders.Collaborate as part... ...g., Python, R, SQL).We are a team of product analysts and data scientists who p…
$175k - $250k
Data Center MEP EngineerAbout MillenniumMillennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our inv…
seeking a skilled Databricks Engineer with minimum of 5+ years of hands-on in Databricks who can design, develop, and optimize scalable data platforms and analytics solutions using the Databricks Lakehouse Platform. The…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
$127.5k - $170.4k
closely with Engineering, Product, and Business teams, we deliver data- driven insights and innovative capabilities that guide our... ...users globally.What is the role?Roku is seeking a Senior Data Scientist to join the…
02
What data scientists earn in New York
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $51–$70 | $106k–$145k |
| Mid level | $70–$95 | $145k–$198k |
| Senior | $92–$127 | $191k–$264k |
Adjusted for the New York market from national ranges.
03
What employers ask for
The skills these listings keep naming
Python (pandas, scikit-learn)SQLStatistics and A/B testingMachine learning fundamentalsData visualizationExperiment designCommunicating with stakeholdersDomain and product sense
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through a data project that changed a decision.
Structure it as question, approach, finding, action. Projects that ended in a decision — even 'we did not launch' — beat technically impressive analyses that went nowhere.
How would you design an A/B test for a new feature?
Cover the metric, randomization unit, sample size, and duration, plus a pitfall like peeking or interference. Practical rigor is what is being tested.
Your model performs well offline but poorly in production. Why might that be?
Discuss train/serve skew, data leakage, distribution shift, and feedback loops. Listing several plausible causes and how you would check each is the strong answer.
Explain p-values or confidence intervals to a non-technical stakeholder.
Use plain language and a concrete scenario, and resist overstating certainty. They are testing whether your statistics survive translation.
How do you handle missing or messy data?
First ask why it is missing — the mechanism matters more than the imputation method. Then discuss options and how the choice affects conclusions.
When would you not use machine learning for a problem?
When a rule, a query, or a simple heuristic wins on cost and interpretability. Knowing when ML is overkill signals maturity.
How do you decide which metric a team should optimize?
Talk about proxy versus true goals, gameability, and counter-metrics. A story about a metric that backfired is very effective here.
05
Resume tips that move the needle
For data scientists specifically — generic advice costs you here
01
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
02
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
03
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
04
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
05
Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
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Where this role goes
Typical progression
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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