$70 - $80 per hour
Data Engineer LaSalle Network is hiring for a Data Engineer with a client that is focused on government solutions and consulting services. LaSalle Network is partnering with our client to hire a Data Engineer in Brentwoo…
Engineering & IT · Nashville, TN
Data Scientist jobs in Nashville, TN
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
706
Open roles today
$38–$96/hr
Typical pay range
$130k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
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Data & Analytics Engineer, ServiceNow - Nashville, 37203, United States of America How we LEAD: The Data & Analytics Engineer, ServiceNow is responsible for delivering trusted reporting, insights, and performance measure…
$60k - $145k
in the life sciences industry. The Migration Consultant will lead customer migration implementations by providing expertise with data and document content migration, Vault API, Vault migration tools, and Vault Platform b…
$58 per hour
Data Engineer Location: Brentwood, TN (Hybrid) Position Type: 12-Month Contract Schedule: Full-Time, 40 Hours per Week (Monday-Friday) Pay Rate: $58/hr About the Opportunity We are looking for a Data Engineer to join a g…
$225k - $265k
projects valued at more than $300 billion. Learn more at [ getbuilt.com .]( Built is looking for a Senior Engineering Manager, Data to lead our growing data organization. In this role, you will guide a high-impact group…
$170k - $245k
Built is looking for a Staff Software Engineer to join our Data Platform team and lead the next generation of customer-facing data... ...working with data engineers, analytics engineers, data scientists, or teams that pr…
$102.68k - $171.13k
Req ID: 384928 NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are…
$20 - $30 per hour
Job Title: Data Processing Analyst - Hybrid Duration (Contract): 12 Months Client Location: Nashville, TN 37243 Location Preference: Hybrid Job Description: As a Data Processing Analyst, you will support the Viral Hepati…
$123k - $163k
Salary: $123,000 - 163,000 per year Requirements: ~7+ years of experience in data engineering for DE III role ~3-7+ years of experience in data engineering for DE II role ~ Advanced to expert-level proficiency in SQL, wi…
$70.8k - $156.7k
Data & Analytics Engineer Position Description Do you want to take your career to the next level? CGI is looking for a dedicated and experienced Data and Analytics Engineer with a passion for solving business problems to…
We compete on results. The Position Translate business needs into scalable analytics solutions, and developing reporting and data products using SQL, QlikView/Qlik Sense, Python, R, and related business intelligence tool…
$66k - $105k
JOB DESCRIPTION Job Title Demand Generation Data Analyst Job Description We’re seeking a marketing analytics professional who partners cross‑functionally to deliver advanced, AI‑driven insights across campaigns and the f…
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What data scientists earn in Nashville
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$72 | $110k–$150k |
| Senior | $70–$96 | $145k–$200k |
National ranges — pay in Nashville typically tracks these.
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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
02
Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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