~ This position is hybrid in Richmond, VA. Our client has an opening for a Oracle APEX Developer / Programmer Analyst 3 (796854) This position is up to 15 months with the option of extension. The client is located in Ric…
Engineering & IT · Richmond, VA
Data Scientist jobs in Richmond, VA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
478
Open roles today
$38–$95/hr
Typical pay range
$129k
Median, full-time
4
Fresh in this list
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01
Open data scientist roles
12 shown of 478 · sorted by freshness
$99.6k - $166k
ETL development, performance tuning in Oracle, and complex SQL In-depth knowledge of Apache Kafka for event-driven and real-time data processing Experience with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn…
$120k - $130k
most important first: Commission mechanical, plumbing & fire alarm and suppression equipment and related controls, primarily in data center environments. Review system designs for compliance with client facility goals. P…
application running on Mod PLSQL gateway. This technology is outdated. All the modules in client capture and deal with highly sensitive data (PII and PHI), so web application security is a high priority aspect for the cl…
$61 - $66 per hour
financial and insurance industries. This is an opportunity to play a key role in building scalable, high-performance web applications and data pipelines that enable critical analytics and insights. You'll design, build,…
Overview Lead Data Engineer (Python, AWS, Spark, Kafka, SQL, Snowflake, Databricks, GenAI) Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, coll…
feasibility studies; analyzing new requirements; preparing functional and system specifications for application programmers; developing data models and system designs; developing complex program codes; testing applicatio…
$197.3k - $225.1k
Overview Lead Data Engineer - Data Publication and Transformation Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and…
$131.3k - $149.8k
Overview Principal Data Analyst - Business Cards & Payments - Field Strategy At Capital One, data is at the center of everything we do. When we launched as a startup we disrupted the credit card industry by individually…
$197.3k - $225.1k
Overview Lead Data Engineer Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Ca…
Richmond, VA (Hybrid) Duration: Through July 31, 2026 with possible extension Role Summary: Seeking a Database Administrator / Data Engineer with experience migrating on-prem SQL Server databases to AWS and Snowflake. Th…
Richmond, Virginia, 23238 CarMax, the way your career should be! About The Team The Pricing team is a community of analysts, data scientists, and systems experts with a variety of technical and strategic skillsets. We wo…
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What data scientists earn in Richmond
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$52 | $79k–$109k |
| Mid level | $52–$71 | $109k–$148k |
| Senior | $69–$95 | $144k–$198k |
Adjusted for the Richmond 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.
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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
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Staff Data Scientist
05
Head of Data Science
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