Senior Data Engineer Location: Chicago, IL or Minneapolis, MN (Hybrid 3 to 4 Days Onsite/Week) Duration: 6 10 Months Contract Interview: Virtual round Technical Skills Azure | Azure Databricks | Azure Data Factory (ADF)…
Engineering & IT · Chicago, IL
Data Scientist jobs in Chicago, IL
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
3,733
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
5
Fresh in this list
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01
Open data scientist roles
12 shown of 3,733 · sorted by freshness
$150k - $200k
members that excel and their contribution is critical to our continued success. Belvedere Trading is looking for a proven Senior Data Engineer to work directly with our trading desks to improve trading performance. This…
Description: Hybrid At least 2 days per week in office in Chicago, IL Our client seeks a Lead Data Engineer to design, build, and optimize large-scale data processing for attribution, measurement, forecasting, and privac…
$150k - $180k
Your Role We are seeking a highly experienced Foundry Lead Data Engineer to architect, develop, and optimize scalable data... ...Functional Collaboration & Leadership • Partner with data scientists, analysts, and busines…
$112.9k - $188.1k
with applicable law. Position Details: As a Senior Data Engineer at Grainger, you will play a pivotal role in designing... ...batch use cases. Partner with cross-functional teams (data scientists, software engineers, pro…
$100k - $115k
Management is an Equal Opportunity Employer (EOE). Position Summary: The Excel Analytics team is growing, and we are seeking a Data Engineer to help build and maintain the data pipelines and platforms that power our repo…
$120k - $160k
flexibility needed for a meaningful work-life balance. Being a Senior Data Engineer at iManage Means… You get excited about data, and... ...across organizational boundaries with data analysts, data scientists, and busine…
$106.5k - $182.6k
Job Description Job Description Provides continuous build-up and operationalization of an enterprise-class modern data environment, which may include various components within the Azure, Hadoop, SQL server, and Informati…
$179.4k - $204.7k
Overview Lead Data Engineer, (Python, AWS) Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery env…
$179.4k - $204.7k
Overview Lead Data Engineer Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Ca…
Lead Data Engineer-Remote Remote (Chicago-area preferred) Visa: EAD/GC/Citizen only. 10+ years of IT experience, including deep expertise in data engineering & ETL; 4-6+ years of recent hands-on experience in designing,…
$154k - $170k
from a deals marketplace to an experience discovery platform that works for customers and merchants at the same time. Groupon's data infrastructure underpins every merchant deal, every customer transaction, and every ope…
02
What data scientists earn in Chicago
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 Chicago 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.
05
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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