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.
745
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
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Open data scientist roles
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JOB SUMMARY: The Senior Analytics Engineer is responsible for designing, building, and maintaining the enterprise data models that power reporting and analytics across C3 Industries and High Profile Cannabis Shops. Lever…
$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…
Technical Project Analyst Hybrid in Philadelphia, PA (4x/week) Contract through 12/31/26 with extensions likely We are seeking a Technical Project Analyst to support a fast-paced technology program focused on product rel…
leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global de…
$122k - $145k
acceptance criteria* Create process maps that demonstrate a client’s business workflows to assist in stakeholder alignment* Capture data, reporting, security, and user experience needs at a functional level* Validate req…
Title: Data 360 Data Conversion Engineer Location: Philadelphia, PA USA (Onsite) Duration: 06+ Month contract role Position Overview We are seeking a Data Conversion Specialist to support leading data migration and conve…
$81.52k - $101.89k
you to bring your civil engineering and land development experience into play as you support the planning, permitting, and design of data center and other large-scale industrial sites. This role will provide hands-on tec…
We are seeking a Senior Data Engineer to design, develop, and maintain scalable data pipelines and data ingestion processes using modern Big Data technologies. The ideal candidate will have strong experience in data aggr…
Primary Responsibilities Design, develop, test, and maintain scalable software applications, APIs, and services. Use AI tools for code generation, debugging, testing, refactoring, and documentation. Review and validate A…
The Data Engineer designs, builds, and maintains reliable data pipelines, models, dashboards, and cloud data infrastructure that transform diverse source data into trusted, reusable data products for analytics, research,…
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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
01
Junior Data Scientist
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Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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