to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility.About the Team The ADAS Data Infrastructure team builds and operates the foundational systems that power autono…
Engineering & IT · Detroit, MI
Data Scientist jobs in Detroit, MI
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
853
Open roles today
$36–$89/hr
Typical pay range
$121k
Median, full-time
4
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Manager is a brand focused creative and brand equity intelligence provider within the Marketing Strategy Intelligence team, ensuring data- informed decision-making across product marketing (left) and marketing strategy (…
connected, shaping the future of transportation on a global scale.. The Role Our Product Safety Data Analytics team is seeking an experienced AI/ML Data Scientist with hands-on experience in the full end to end data scie…
more connected, shaping the future of transportation on a global scale. The Role The Resource Demand team is building trusted data pipelines, analytical datasets, and reporting applications that improve engineering plann…
$4,600 - $5,400 per month
Company: Fierce Staffing Services Location: Remote (with occasional on-site event coverage) Employment Type: Contract-to-Hire (W2) Reports To: Arielle Johnson, Founder & CEO About Fierce Staffing Services: Fierce Staffin…
Job Description Job Description Job Title: Data & Reporting Analyst Location : Southfield, MI (Fully in-person) Job type : Full Time Role Summary The Data & Reporting Analyst is responsible for transforming complex opera…
portfolio analytics experience - A minimum of three (3), and preferably four to seven (4-7) years, of professional experience in data engineering, business intelligence, data analytics, or a related information technolog…
$150.5k - $245.5k
on more meaningful work. We focus on developing and improving data pipelines, infrastructure, architecture, and analytic tools to... ...transformation. Working with a team of engineers, analysts, and scientists, you will…
Band: Professional Job Summary The EGTM Analyst owns the measurement and monitoring of the GTM Blueprint, turning performance data into clear insights, action plans, and enterprise visibility. This role compiles and publ…
a technical expert and project leader for the most challenging data science projects. Provides highly technical and analytical assessments... ...decision-making culture. Oversees and coaches the team of data scientists t…
Job Description Job Description About Us At Proactive Technology Management, we're transforming how businesses harness data to drive innovation and informed decision-making. As leaders in the SMB space, we leverage cutti…
$140k - $200k
office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD... ...them within 1 week. Overview We're looking to hire for our Data side of our A…
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What data scientists earn in Detroit
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $36–$49 | $74k–$102k |
| Mid level | $49–$67 | $102k–$140k |
| Senior | $65–$89 | $135k–$186k |
Adjusted for the Detroit 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
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Head of Data Science
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