$160k - $170k
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. Essential Duties and Res…
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.
1,104
Open roles today
$40–$101/hr
Typical pay range
$136k
Median, full-time
6
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Open data scientist roles
12 shown of 1,104 · sorted by freshness
Archesys Inc seeks a highly skilled Backend-leaning Ruby on Rails Engineer to join the Financial Data Experience Team. You will build foundational payment and accounting integrations for a greenfield Rails app, including…
$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…
$85k - $112.5k
Job Description The Information Security Engineer will be a part of the enterprise Data Security and Governance, Risk, & Compliance program with focus on developing and scaling data security controls, insider risk manage…
$89.6k - $198.4k
Data Engineer Position Description CGI Federal is seeking a Data Engineer to support a federal agency in modernizing its fraud... ...engineering solutions while collaborating closely with data scientists, analysts, and f…
Data Engineer / DBA / Backend Engineer – FACETS Data Location: Baltimore, MD / Washington, DC (Hybrid) Type: Contract-to-Hire Schedule: 40 Hours/Week (EST) Industry: Healthcare / Health Insurance Work Authorization: Must…
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…
traditionally defined problem space. We bring Public and Private, Civilian and Military expertise to every case. We are hiring a Sr. Data Scientist to work in Linthicum Heights, MD . Position location is subject to chang…
defined problem space. We bring Public and Private, Civilian and Military expertise to every case. We are hiring a Software Engineer ( Data Management) in Linthicum Heights, MD. Position location is subject to change bas…
Job Description Job Description Seeking a Senior Data Scientist (NLP) to join our team in Woodlawn, MD supporting a large federal agency. This role requires deep expertise in Natural Language Processing (NLP) and Generat…
$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…
$220k - $230k
computer science). ~10 years of experience analyzing datasets and developing analytics ~10 years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. Job Description: ~ As part of the…
02
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.
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.
03
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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