Senior Data Engineer - Philadelphia, 19109, United States of America How we LEAD: We are seeking an experienced and driven Senior... ..., and usability for downstream applications. Work with data scientists and analysts…
Engineering & IT · Philadelphia, PA
Data Scientist jobs in Philadelphia, 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.
910
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
6
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Open data scientist roles
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wherever you want it to go. Join EY and help to build a better working world. Manager - Financial Services Organization – AI and Data - Service Delivery Center EY is the only professional services firm with a separate bu…
$150k - $160k
Job Description The Director, Data Engineering & AI Enablement is the senior-most technical leader and active builder for the data... ...a cross-functional team of data engineers, ML engineers, data scientists, data arch…
$132.9k - $182.7k
Electrical Engineer in our Advanced Manufacturing group, you’ll contribute to projects that enable the heart of our clients’ successful data center facilities engineering, design and construction. In this role, you will…
$123.6k - $162.3k
Industry Advanced Manufacturing At Jacobs, we're challenging today to reinvent tomorrow by solving the world's most critical problems for...
$169.5k - $233k
we never settle — we push boundaries, elevate standards, and deliver with purpose. As a senior subject matter expert within our Data Center team, you’ll lead structural engineering efforts across a portfolio of data cent…
$132.9k - $182.7k
near a JACOBS U.S. based office, but we intend to hire the "best" candidates. We're looking for a Senior Civil Engineer to join our Data Center group, and you'll have the chance to work on projects including state-of-the…
$169.5k - $233k
inspires and empowers you to deliver your best work so you can evolve, grow, and succeed – today and into tomorrow. In our Mechanical Data Center team, we elevate our clients by delivering cost-effective and transformati…
$100k - $130k
assets that meet their needs as effectively and then efficiently as possible. To achieve this vision, Aramark will deliver strategic data assets that enable our operators to maximize the hospitality experience for our co…
you want it to go. Join EY and help to build a better working world. Senior Analyst - Financial Services Organization – AI and Data – Service Delivery Center EY is the only professional services firm with a separate busi…
Junior Data Scientist- Philadelphia, Data Science – Sports + Entertainment Aramark Sports + Entertainment is hiring a Junior Data Scientist based in Philadelphia to support our Sports + Entertainment portfolio, including…
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What data scientists earn in Philadelphia
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $41–$57 | $86k–$119k |
| Mid level | $57–$78 | $119k–$162k |
| Senior | $75–$104 | $157k–$216k |
Adjusted for the Philadelphia 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
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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
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Junior Data Scientist
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Data Scientist
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Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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