We are seeking a Snowflake Data Analyst/Engineer with strong expertise in Snowflake, SQL, and data pipeline validation. The ideal candidate will be responsible for designing and optimizing data models, validating data pi…
Engineering & IT · Washington, DC
Data Scientist jobs in Washington, DC
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
9,441
Open roles today
$48–$120/hr
Typical pay range
$162k
Median, full-time
3
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01
Open data scientist roles
3 shown of 9,441 · sorted by freshness
$128.04k - $173.23k
Currently Possess: None Clearance Level Must Be Able to Obtain: None Public Trust/Other Required: None Job Family: Data Science and Data Engineering Job Qualifications: Skills: Data Analysis, Data Analytics, Data Lake, D…
We are looking for Sr Data Engineer in Lansing MI Hybrid . Please read the job description below and let me know if you are interested. Position: Senior Data Engineer Location: Lansing, MI (Hybrid Local Candidates Only)…
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What data scientists earn in Washington
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $48–$66 | $100k–$138k |
| Mid level | $66–$90 | $138k–$188k |
| Senior | $87–$120 | $181k–$250k |
Adjusted for the Washington 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
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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.
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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