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Engineering & IT · Pittsburgh, PA
Data Scientist jobs in Pittsburgh, PA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
798
Open roles today
$38–$93/hr
Typical pay range
$126k
Median, full-time
5
Fresh in this list
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01
Open data scientist roles
11 shown of 798 · sorted by freshness
$71.3k - $140.6k
clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation. Work you'll do As a PROJECT - Data Engineer II on the AI & Data team, you will be responsible for… Designing, de…
As Data Analyst, USCA Transportation, you are responsible for supporting transportation and logistics operations through data analysis, performance measurement, reporting, and business intelligence initiatives. You serve…
$100k - $120k
Business Analyst with strong business analysis and stakeholder management capabilities to act as the bridge between business teams and data engineering organizations.Work Location: Pittsburgh, PAWork Mode: Hybrid (2-3 da…
journey. As we modernize our operations and unlock the power of data across our business, we are seeking a skilled and driven Data... ...robust APIs and data services, and collaborate closely with data scientists, analys…
Data Engineer – Azure Databricks Contract-to-Hire Pittsburgh, PA – Onsite Job ID J0726-0569 Visa : USC, GC, EAD (No Sponsorship) Position Overview Seeking an experienced Data Engineer to join a high-performing data engin…
Job Title: Senior AWS Data Engineer Location: Knoxville, TN, Columbia SC or Lafayette, LA or Birmingham, AL Description: Senior AWS Data Engineer to support the development, enhancement, and maintenance of enterprise ris…
$70.8k - $156.7k
Data Engineer Position Description This role will require someone at our client site 5 days a week preferably in Pittsburgh, PA, We are seeking a Data Engineer with 6+ years of experience to design and maintain scalable…
$70.8k - $156.7k
Senior Data Engineer Position Description This role will require someone at our client site 5 days a week in Pittsburgh, PA, We are seeking a Data Engineer with 6+ years of experience to design and maintain scalable data…
Position Title: Data Engineer Location: PA – Pittsburgh (Locals Preferred) Work Status : Onsite 5 days a week Duration : Contract to Hire Years Of Experience Required : 6+ Years Industry Background: Finance / Banking Fun…
Data Ideology At DI, we provide Data & Analytics expertise to drive measurable business outcomes, often solving complex business problems for our clients. Our data analytics advisory services enable our customers to tran…
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What data scientists earn in Pittsburgh
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$51 | $78k–$107k |
| Mid level | $51–$70 | $107k–$146k |
| Senior | $68–$93 | $141k–$194k |
Adjusted for the Pittsburgh 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
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.
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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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