Data Engineering Engineer 3 Location: 679 - Rotunda Center Location Address: 17000 Rotunda Drive, DEARBORN, MI, 48120 Position Description: We're seeking a highly skilled and experienced Full Stack Data Engineer to play…
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.
564
Open roles today
$36–$89/hr
Typical pay range
$121k
Median, full-time
7
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Open data scientist roles
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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…
Job Title: Sr. Data Engineer HYBRID in Detroit, MI (3 days per week in office req.) NO REMOTE Work Authorization: USC/GC only (W2) Contract to Hire after 6 months Job Summary: We are looking for a Senior Data Engineer to…
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…
Job Title: Data Engineer Location: ONSITE in Detroit, MI Skills : Are you passionate about data, architecture, software development, and analytics? Do you bring deep experience with cloud technologies, data warehousing,…
Description The Role The GM Quality Connected Customer Continuous Improvement team is seeking a data scientist to support digital transformation and AI enablement efforts. The individual will work as a member of a team w…
Accenture's SAP Analytics practice, you will be a member of a delivery teams and focus on client engagements centered on SAP's modern data and analytics platform — including SAP Datasphere, SAP Analytics Cloud (SAC), and…
ROLE_DESCRIPTION: Key Responsibilities Design, develop, and maintain robust and scalable data pipelines for data ingestion, transformation, and loading. Build and optimize data models and transformation workflows using D…
Data Scientist Must Have Technical/Functional Skills Good understanding of machine learning including with extensive hands-on experience in model building (gradient boosting (XGBoost/LightGBM), GLMs, time series, NLP etc…
$85.39k - $116.98k
Syms Strategic Group (SSG) is seeking a talented Senior Systems Engineer (Amazon Web Services (AWS) Data Engineer) - II Location: Remote Department: Veterans Affairs (VA) Type: Full Time Min. Experience: Experienced Secu…
care, advancing health outcomes, and building healthier communities for all. Job Description GENERAL SUMMARY: Lead Data Scientist, Healthcare Analytics works with system leaders, business stake holders and business data…
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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
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.
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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
02
Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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