We are looking for a Data Engineer to join a fast-paced IT consulting environment in Atlanta, Georgia. In this role, you will design and optimize modern data solutions that support analytics, machine learning, and busine…
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.
1,607
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
11
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01
Open data scientist roles
12 shown of 1,607 · sorted by freshness
$205.32k
CompanyCox Automotive - USAJob Family GroupEngineering / Product DevelopmentJob ProfileSr Lead Data EngineerManagement LevelSr Manager - Non People LeaderFlexible Work Option Hybrid - Ability to work remotely part of the…
Inspire Brands is hiring a Lead Data and AI Engineer for the Enterprise Data Organization to design, build and manage data pipelines (Data ingestion, data transformation, data distribution, quality rules, data storage et…
Things (IoT), cloud computing, artificial intelligence (AI), and data analytics to create integrated solutions that improve... ...and with an unending thirst for learning. As an Advance Data Scientist, you will join a hi…
$87.7k - $139.7k
experienced Senior High Voltage Electrical Engineer to lead the design and engineering of critical electrical infrastructure for hyperscale data centers and mission-critical facilities. This role provides technical leade…
$202k - $229k
Job Description Summary: At The Coca-Cola Company, we believe data is the foundation for creating personalized experiences and powering sustainable growth in today’s digital-first world. As consumers engage with our bran…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
$40 - $45 per hour
Data Center Field Engineer Dell PowerEdge Servers (Travel Team)OverviewJoin a high-impact team supporting some of the most advanced AI and enterprise computing environments in North America. We are seeking experienced Da…
Job-ID28042269Reference26-10940 Modern Data Engineer (Enterprise + Lakehouse + AI‐Assisted) Experience 5+ years working with data in a software or data engineering role Experience in enterprise environment…
$164k - $193k
position, you will embark on a journey of leveraging vast amounts of data to transform it into actionable insights. You will aid in the... ...and analytics. Mentor, guide, and develop junior/aspiring data scientists acro…
$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…
We are seeking an experienced Technology Lead / Azure Data Engineer with strong hands-on expertise in Azure Synapse Analytics and PySpark. The ideal candidate will have a deep understanding of Apache Spark, distributed d…
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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
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.
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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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