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Data Science Machine Learning Jobs in Atlanta, GA

Responsibilities • Collaborate with team members to develop and deploy machine learning models and data science solutions. • Work with Product Management to understand requirements and translate ...

Responsibilities • Collaborate with team members to develop and deploy machine learning models and data science solutions. • Work with Product Management to understand requirements and translate ...

Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field * 5-10 years of experience in data science, machine learning, and statistical analysis

Key Responsibilities Design, develop, and deploy machine learning models and scalable data science solutions Partner with Product Management to translate business requirements into analytical ...

Experience • 5+ years of experience in data science, machine learning, or related analytical roles. • 5+ years of experience with Python and data science libraries (pandas, NumPy, scikit-learn ...

AI Machine Learning Scientist AI Machine Learning Scientist Location: This role requires associates ... Will work closely with engineering, product, data science, and business teams to translate complex ...

Discovery-serving as a company-wide authority in advanced Data Science, Machine Learning, and Applied AI. This role is designed for an elite practitioner with 15-18+ years of experience, including 10 ...

Establish and promote best practices in data science, machine learning, experimentation, model governance, and MLOps throughout the organization. * Lead proof-of-concept (POC) initiatives to evaluate ...

Establish and promote best practices in data science, machine learning, experimentation, model governance, and MLOps throughout the organization. * Lead proof-of-concept (POC) initiatives to evaluate ...

Degree in Data Science, Machine Learning, Applied Mathematics/Statistics, or a related field. * 3 years of experience applying data science, AI/machine learning, or analytics techniques to business ...

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Data Science Machine Learning information

See Atlanta, GA salary details

$36.1K

$118K

$189K

How much do data science machine learning jobs pay per year?

As of Jun 29, 2026, the average yearly pay for data science machine learning in Atlanta, GA is $118,032.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,700.00 and $130,800.00 per year, depending on experience, location, and employer.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
Azure Data scientist

Full-time

Posted 6 days ago


Key responsibilities

  • Design and create a suitable working environment for data science workloads.

  • Explore data and train machine learning models.

  • Manage, deploy, and monitor scalable machine learning solutions.


Job description

Overview:
Role: Azure Data scientist
Location: Atlanta GA
#Role is on-site, Must relocate
Must w2 Role
should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.
Your responsibilities for this role include:
  • Designing and creating a suitable working environment for data science workloads.
  • Exploring data.
  • Training machine learning models.
  • Implementing pipelines.
  • Running jobs to prepare for production.
  • Managing, deploying, and monitoring scalable machine learning solutions.
  • Using language models for building AI applications.

Skills:
Azure,Data Science,Machine Learning