$135k - $140k
Description: Hi , Hope you are doing well! This is OpenKyber. I was just trying to reach you for an opportunity we have for Senior Data Engineer- Chicago, IL Hybrid So just wondering if you are looking for any new opport…
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.
5,684
Open roles today
$51–$127/hr
Typical pay range
$172k
Median, full-time
1
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01
Open data scientist roles
9 shown of 5,684 · sorted by freshness
$130k - $185k
the way – enabling you to shape your future with confidence. Within the EY-Parthenon service line, the EY Growth Platforms Data Scientists collaborate with Business Leaders, AI/ML Engineers, Project Managers, and other t…
Overview Lead Data Engineer (Python, AWS, Spark, Kafka, SQL, Snowflake, Databricks, GenAI) Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, coll…
Snowflake Lead – Data Engineering San Antonio, TX / New York, NY (Onsite) Local to San Antonio, TX / New York, NY only! P&C / Specialty Insurance exp Must Must-Have Skills – Snowflake Lead (Data Engineering) * 12+ years…
education in underserved communities and helping organizations achieve their full potential with AI. About the role A Lead Data Scientist is responsible for designing and implementing data-driven solutions to complex bus…
$193k - $242k
at BlackLine! Make Your Mark: We’re looking for a Lead Data Engineer to design, build, and optimize data pipelines that power... .... Collaborate with cross-functional teams, including data scientists, analysts, and busi…
$155.7k - $208.7k
future. Disney Sports News & Entertainment is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for…
honestly. The Role TeamBuilder builds software for ambulatory healthcare operations. The problem is simple: turn messy healthcare data into actions. This role sits between data, forecasting models, and optimization. You…
Join a dynamic team committed to excellence in the Big Data and Analytics sector. Our client is seeking an Analytics Engineer to play an integral role in their innovative team based in London. This permanent, hybrid posi…
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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.
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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.
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Resume tips that move the needle
For data scientists specifically — generic advice costs you here
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Lead every bullet with the business result — revenue, retention, cost — and put the method second.
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Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
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Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
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Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
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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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