$118.3k - $177.45k
advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, we’re making it easier for buyers and... ....Job SummaryFreewheel is currently looking to recruit a Data Scientist to…
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.
1,884
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
9
Fresh in this list
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01
Open data scientist roles
12 shown of 1,884 · sorted by freshness
critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your wor…
$145k - $165k
OverviewAs a Lead of Analytics Engineering at Avison Young Technologies, you will lead the strategy and execution of our proprietary data products and models that empower commercial real estate decision makers across inv…
$125k - $140k
Chicago Business multiple times and is one of Barron’s Top 100 RIA firms.Mesirow is looking for a partner to support and modernize our data architecture with the vision of establishing a modern data estate. This is a hig…
$156k - $234k
Dir Data Engineering - GE06AEWe’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages... ...assessment preferred.Ability to partner with actuaries, data scientists, a…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
$100k - $200k
absolute integrity. East West Bank gives people the confidence to reach further. Overview The Financial Crime Risk Assessment Data Scientist is responsible for supporting the development, maintenance, and enhancement of…
$85.6k - $149.4k
The Clinical Informaticist combines clinical expertise, healthcare informatics, and data science to support the development, implementation, and optimization of Wolters Kluwer Health solutions. This role applies knowledg…
Analyze data to identify trends, detect variances, explain root causes, and support defect remediation using statistical and regression analysis. Responsibilities: · Build and validate regression models to explain data d…
Responsibilities: Design and develop scalable data pipelines for data extraction, transformation, integration, and loading.... ...optimization techniques. Collaborate with data architects, data scientists, AI engineers,…
Are you looking for an exciting new opportunity? Join a world-class trading firm where some of the brightest traders, engineers, and researchers collaborate to solve one of the most challenging problems in global markets…
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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.
05
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.
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
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Staff Data Scientist
05
Head of Data Science
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