$60k - $100k
year Requirements: Strong background in Python, SQL, and core data engineering practices Hands-on experience building ETL/ELT... ..., and troubleshooting procedures Partner with Data Scientists, Analysts, Business Teams,…
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.
904
Open roles today
$38–$93/hr
Typical pay range
$126k
Median, full-time
1
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01
Open data scientist roles
11 shown of 904 · sorted by freshness
Skills & Experience • Strong hands-on experience with Snowflake (tables, views, performance tuning) • Advanced SQL skills and data transformation expertise • Experience building data ingestion pipelines (ETL/ELT) • Exper…
$70.8k - $156.7k
Data Engineer Position Description We are seeking a Data Engineer with 5 years of experience to design and maintain scalable data pipeline supporting analytics, reporting, and operational needs. The role involves collabo…
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…
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…
$79.6k - $139.3k
Data Engineer - Databricks - Local to Pittsburgh, PA Position Description Join a high performing data engineering team responsible for building modern, cloud native data platforms on Microsoft Azure. This role offers the…
and executing at the speed of operational demands, the front line gets what it needs to succeed. Job Description The Lead Data Scientist will provide visionary technical leadership to a team of data scientists and AI eng…
speed of operational demands, the front line gets what it needs to succeed. Job Description We are seeking an inquisitive data scientist to join our team and work with our various datasets to find connections, knowledge,…
speed of operational demands, the front line gets what it needs to succeed. Job Description We are seeking an inquisitive Data Scientist to join our team, leveraging deep expertise in Artificial Intelligence and a keen u…
speed of operational demands, the front line gets what it needs to succeed. Job Description We are seeking an inquisitive Data Scientist to join our team, leveraging deep expertise in Artificial Intelligence and a keen u…
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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.
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.
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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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