Title: Data Analyst & Power Bi Engineer Location: Remote Let's create our future together at The AES Group! About The AES Group: The AES Group is a premier technology and engineering consulting company that has been brin…
Engineering & IT · Atlanta, GA
Data Scientist jobs in Atlanta, GA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
2,541
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
4
Fresh in this list
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01
Open data scientist roles
12 shown of 2,541 · sorted by freshness
complex business problems and to design end-to-end analytical solutions that will improve their existing processes and ability to derive data- driven insights? Aimpoint Digital is a dynamic and fully remote AI and Data c…
Stord Inc. is seeking a Lead Data Scientist based in Atlanta, GA. This role involves leading the development of digital twin models and embedding AI systems into operational workflows. You will work closely with a five-p…
impact role where youll own and modernize the infrastructure behind institutional client reporting? TRC Talent Solutions is hiring a Data & Reporting Engineer for a growing investment management firm in Atlanta, GA . The…
Data Engineer Location: Atlanta, GA (100% Onsite) Duration: 6 Months Contract Interview: Virtual round... ...Collaborate with business stakeholders, analysts, and data scientists to deliver high-quality data solutions. I…
$155k - $165k
frontier is real-time pricing at scale—the Alt Value that powers every trade, loan, and product on the platform. The Role Are you a data engineer who thrives on building robust pipelines and solving complex data challeng…
$150k - $170k
Lead Growth Data Scientist Office Locations: San Diego, CA (La Jolla/UTC) or Atlanta, GA (Cumberland/Galleria) or Orlando, FL (Maitland, FL) Hybrid 2 days per week onsite in the office (Mondays and Thursdays), Full time…
$150k - $170k
Lead Data Scientist - Growth & Marketing Models AI-first targeting and decision models that move real money | Lean, AI-leveraged team | Senior/Lead level Office Locations: San Diego, CA (La Jolla/UTC) or Atlanta, GA (Cum…
USC & GC Preferred Job Summary We are looking for a Data Engineer to design and maintain enterprise data platforms that... ...and distribution. Collaborate with business analysts, data scientists, and stakeholders to und…
We are looking for a motivated entry-level professional with a strong foundation in Computer Science and a passion for working with data. This role is ideal for recent graduates who have completed academic projects invol…
Job Description: We are looking for early-career Data Analytics / Data Science professionals who are passionate about working with data and deriving meaningful insights. Candidates with internship experience, academic pr…
$147k
Job Description Job Description Senior Data Engineer Location: Preference will be given to candidates located in Atlanta, GA or... ...efficient decision-making. You will work alongside a team of data scientists, operatio…
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What data scientists earn in Atlanta
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $39–$54 | $82k–$112k |
| Mid level | $54–$74 | $112k–$153k |
| Senior | $71–$98 | $148k–$204k |
Adjusted for the Atlanta 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
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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.
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Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
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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
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Senior Data Scientist
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Staff Data Scientist
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Head of Data Science
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