$73k - $113k
Responsibilities: Design, build, implement, and maintain ETL data pipelines across multiple data sources Develop scalable... ...infrastructure and processing Collaborate with engineers, data scientists, analysts, product…
Engineering & IT · Baltimore, MD
Data Scientist jobs in Baltimore, MD
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
550
Open roles today
$40–$101/hr
Typical pay range
$136k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
12 shown of 550 · sorted by freshness
Note: This position is contingent on contract award. NOVACES is seeking a Senior Data Scientist for a U.S. Coast Guard Surface Forces Logistics Center Business Operations Division support opportunity in Baltimore, MD. Th…
$209k - $224k
process to a new model that emphasizes automation, streamlined processes and approvals, continuous monitoring and assessment, and network data gathering across the entire life cycle of a project. Leverage Python as a pri…
$143.91k
Salary: Starting at $143,913 Per year (GS 14)Dates: Open 09/21/2026 to 10/02/2026Schedule: Full-timeWork type: PermanentRelocation: FalsePosition ID: CMS-CPI-26-13069755-ITDocument ID: 885478400Grade: GS 14Job category:…
$120.8k - $265.8k
process to a new model that emphasizes automation, streamlined processes and approvals, continuous monitoring and assessment, and network data gathering across the entire life cycle of a project.Responsibilities:The Data…
including developing and running automated unit tests, drafting, and executing structured automated test cases. Compile, assess, and report data results from software testing and analysis. 25% Develops software solutions…
Ld PURPOSE: Responsible for developing, modifying, and executing software test plans, test scenarios, Defines and solves for test data needs, develops automated scripts and programs for testing in order to ensure the out…
Responsibilities : Data Scientist: Systems engineering with a focus on complex systems of systems; Data Science, including natural language processing (NLP) and machine learning (ML); Data extraction and processing from…
$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…
$125k - $165k
teammate, apply to this position on the Internal Career Site Here. Purpose of Role Under Armour is seeking a highly motivated Sr. Data Analytics Engineer to join our Enterprise Data Management & Analytics team. This role…
$100k - $150k
Job Title: Data Engineer Location-Type: Hybrid / Travel – Baltimore, MD (50–75% onsite) Work Hours: 40 Hours/Week Start... ...scalability, reliability, and cost Partner with engineers, data scientists, technical teams, a…
$89.6k - $198.4k
e-Discovery Data Migration Engineer Position Description CGI is seeking an experienced eDiscovery Data Migration Engineer/Specialist to support complex legal data transitions between enterprise eDiscovery platforms. The…
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What data scientists earn in Baltimore
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$56 | $84k–$116k |
| Mid level | $56–$76 | $116k–$158k |
| Senior | $73–$101 | $152k–$210k |
Adjusted for the Baltimore 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.
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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.
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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
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Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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