$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,…
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.
476
Open roles today
$38–$95/hr
Typical pay range
$129k
Median, full-time
6
Fresh in this list
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01
Open data scientist roles
12 shown of 476 · sorted by freshness
$180k - $200k
Business Units through the effective identification, mitigation, monitoring and reporting of transverse AI risk (e.g., technology, data, operational, compliance) within Enterprise Technology. 2. Serve as a subject matter…
Hello All, I hope you're doing well. Role: Lead Data Engineer - SQL Server / ETL / NEIEN XML / Migration Location: U.S.-based / onshore. Most work may be performed from the Supplier's location, with onsite presence at Vi…
reviewed at critical points.Work is done independently and is reviewed at critical points.Workplace type: Remote WorkingAbout NTT DATANTT DATA is a $30+ billion trusted global innovator of business and technology service…
$80.46k - $142.72k
deliver more impact together.Role description:As an Electrical Engineer you will lead the electrical discipline of multiple concurrent data center projects through pursuit, proposal, design, and construction phases. You…
Job-ID27855667Reference26-08833Required: Epic Willow Ambulatory (WAM) or Willow Inpatient (WIP)Graduate of an accredited School of Pharmacy with a B.S. in Pharmacy or Pharm.D. DegreeThe Application Analyst-Pharmacist par…
$209k - $238.5k
Overview Data Engineer 5 Do you love building and pioneering in the technology space? Do you enjoy solving complex business... ...technologies Influence a team of developers, data analysts and data scientists with deep e…
Role: Data Engineer Location: St louis, MI/ Richardson, TX/ Chicago, IL Term: Contract Skills : data bricks . azure, Scala, python. spark
$209k - $238.5k
Overview Data Engineer 5 (Python, SQL, Databricks, Snowflake) (Enterprise Platforms Technology) Position Overview We are... ...pipelines, and predictive ML models. You will work closely with Data Scientists, Analysts, an…
$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…
Job Summary We are seeking an experienced Oracle JD Edwards (JDE) Lead Developer with strong technical expertise in JDE EnterpriseOne development, system architecture, integrations, and troubleshooting. The ideal candida…
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…
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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.
03
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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