$75k - $90k
writing RPG, including RPG III, RPG IV, and RPG Free, for IBM iSeries or AS/400 systems. We require strong SQL query skills for data extraction from DB2 or MySQL. We require experience with a scripting language such as P…
Engineering & IT · Milwaukee, WI
Data Scientist jobs in Milwaukee, WI
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
404
Open roles today
$37–$91/hr
Typical pay range
$124k
Median, full-time
5
Fresh in this list
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01
Open data scientist roles
12 shown of 404 · sorted by freshness
$131.6k - $249.78k
Salary: $131,600 - 249,780 per year Requirements: ~15+ years of relevant experience in the design, development, and evaluation of Data Platform solutions such as Data Warehouses, Data Lakes, and Data Products ~ Advanced…
park—a welcoming greenspace open to all. Join our team as a Sr Data Engineer. Job Summary: We are looking for a skilled Sr... ...AI models to production environments. Work closely with data scientists to ensure model rea…
leads the management and analysis of product development processes to improve efficiency and accuracy while enabling enabling informed, data- driven decision-making. This role partners with program teams and functional m…
court documents, judgments, and other relevant records. Our Research Analyst will: Analyze and validate critical content and data points relating to subject matter experts Research and evaluate information from multiple…
developers, QA, and product management to deliver impactful tools for our financial advisors. The role involves mapping and modeling data, collaborating across many teams at Baird, and partnering with vendors to bring so…
$117.5k - $249.78k
0 - 249,780 per year Requirements: We look for 10+ years of professional experience applying AI/ML to deliver strong commercial data science outcomes. We need expertise with electronic health records or unstructured data…
complex business needs into efficient, scalable solutions. You will leverage your expertise in ECC/IM/EWM configuration, integration, and data analysis to identify improvement opportunities and ensure successful implemen…
help run what runs the world. This position reports to: Pricing & Quotations Manager __ The Senior Revenue Management Data Scientist will transform pricing, quotation, customer, product, and market data into actionable i…
and actuarial firm that develops and deploys category-defining, data- driven, software-as-a-service (SaaS) products for a broad... ...Electronic Health Records or unstructured data analysis ~ Expert data scientist with d…
$52.3k - $68.7k
Protective Intl & Event Security. What You’ll Be Brewing: ~ Monitor open-source intelligence (OSINT), social media, and internal data sources for indicators of threats to personnel, facilities, or events. ~ Analyze threa…
company's goals, values, and objectives, and use this understanding to inform strategy development. Conduct market research and data analysis to identify trends, opportunities, and areas for improvement. Work with cross-…
02
What data scientists earn in Milwaukee
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $37–$50 | $76k–$104k |
| Mid level | $50–$68 | $104k–$142k |
| Senior | $66–$91 | $138k–$190k |
Adjusted for the Milwaukee 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
04
Staff Data Scientist
05
Head of Data Science
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