Machine Learning Manager Jobs in Georgia
Machine Learning Manager jobs in Georgia are in strong demand, with hiring concentrated in financial technology, logistics, healthcare informatics, and defense contracting, covering the full range from team lead through senior director. Atlanta anchors the market alongside Alpharetta and Augusta, where employers such as NCR Voyix, Cox Enterprises, and Anthem anchor recurring demand for managers who can oversee model deployment, MLOps pipelines, and applied AI teams. The most sought-after specializations in Georgia listings are natural language processing, computer vision, and recommendation systems. Find a role that fits below and apply directly.
Find Machine Learning Manager JobsOverview
Showing 5 of 14+ Machine Learning Manager jobs
The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.
This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes.
The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.
Core Accountabilities
Product Model & Discovery Partnership
- Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
- Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
- Translate business and customer questions into durable data models and segmentation frameworks.
Machine Learning
- Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies
- Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
- Collaborating with data scientists
- managing standards for model life cycle, monitoring drift, retraining, etc.
- Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines
Data Platform & Segmentation Architecture
- Define and evolve the segmentation architecture across customer and commercial data domains.
- Design scalable data models that support real-time, near real time, and batch segmentation use cases.
- Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
- Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
Engineering Execution & Data Quality
- Build and maintain high-quality, production-grade data pipelines and services.
- Ensure strong standards for data quality, lineage, observability, and reliability.
- Reduce fragmentation and duplication in segmentation logic across teams.
- Leverage metrics to continuously improve data freshness, accuracy, and usability.
Individual Contributor Technical Leadership
- Act as a go-to expert for data platform and segmentation design.
- Lead complex technical initiatives end-to-end through hands-on contribution.
- Influence technical direction through design reviews, reference implementations, and documented standards.
- Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).
Microsoft Azure Data Platform & Fabric Expertise
- Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments.
- Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
- Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
- Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
- Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
- Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
- Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
Business Partnership & Communication
- Serve as a trusted technical partner to Customer and Commercial stakeholders.
- Communicate segmentation concepts, assumptions, and limitations in clear business language.
- Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
- Support external partner and vendor conversations as a technical authority when needed.
Governance, Privacy & Compliance
- Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
- Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
- Advocate for responsible and ethical use of customer and commercial data.
Success Measures
- Segmentation capabilities measurably improve customer engagement and commercial outcomes.
- Reduced duplication and inconsistency in segmentation logic across products.
- Improved data quality, freshness, and trustworthiness.
- Faster time-to-insight and activation for product teams.
- Platforms and models that scale with growth while controlling cost and risk.
Leadership Profile
- Outcome-driven, not data for data’s sake
- Deep technical expertise with strong product intuition
- Influences through credibility and clarity, not authority
- Comfortable operating in ambiguity and evolving problem spaces
- Holds a high bar for data quality, ethics, and reliability
Required Experience & Capabilities
- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
- 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
- Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
- Strong experience with cloud-native data platforms and modern data tooling.
- Proven ability to partner closely with product and business stakeholders.
- Demonstrated impact as a senior individual contributor on complex, cross-team initiatives.
Pay Range:
United States: 152,000 - 178,300 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
15Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
September 22, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.Pay Range:: 0 - 0 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Long-term Incentive Reference Value Percentage:0 - 20
Long-term Incentive reference value is a market-based competitive value for your role.
The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model.
This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes.
The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management.
Core Accountabilities
Product Model & Discovery Partnership
- Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
- Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
- Translate business and customer questions into durable data models and segmentation frameworks.
Machine Learning
- Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies
- Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products
- Collaborating with data scientists
- managing standards for model life cycle, monitoring drift, retraining, etc.
- Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines
Data Platform & Segmentation Architecture
- Define and evolve the segmentation architecture across customer and commercial data domains.
- Design scalable data models that support real-time, near real time, and batch segmentation use cases.
- Ensure segmentation logic is reusable, explainable, and consistent across channels and products.
- Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
Engineering Execution & Data Quality
- Build and maintain high-quality, production-grade data pipelines and services.
- Ensure strong standards for data quality, lineage, observability, and reliability.
- Reduce fragmentation and duplication in segmentation logic across teams.
- Leverage metrics to continuously improve data freshness, accuracy, and usability.
Individual Contributor Technical Leadership
- Act as a go-to expert for data platform and segmentation design.
- Lead complex technical initiatives end-to-end through hands-on contribution.
- Influence technical direction through design reviews, reference implementations, and documented standards.
- Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility).
Microsoft Azure Data Platform & Fabric Expertise
- Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments.
- Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
- Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
- Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
- Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
- Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
- Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty.
Business Partnership & Communication
- Serve as a trusted technical partner to Customer and Commercial stakeholders.
- Communicate segmentation concepts, assumptions, and limitations in clear business language.
- Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
- Support external partner and vendor conversations as a technical authority when needed.
Governance, Privacy & Compliance
- Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
- Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
- Advocate for responsible and ethical use of customer and commercial data.
Success Measures
- Segmentation capabilities measurably improve customer engagement and commercial outcomes.
- Reduced duplication and inconsistency in segmentation logic across products.
- Improved data quality, freshness, and trustworthiness.
- Faster time-to-insight and activation for product teams.
- Platforms and models that scale with growth while controlling cost and risk.
Leadership Profile
- Outcome-driven, not data for data’s sake
- Deep technical expertise with strong product intuition
- Influences through credibility and clarity, not authority
- Comfortable operating in ambiguity and evolving problem spaces
- Holds a high bar for data quality, ethics, and reliability
Required Experience & Capabilities
- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
- 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
- Deep expertise in data modeling, segmentation strategies, and large-scale data systems.
- Strong experience with cloud-native data platforms and modern data tooling.
- Proven ability to partner closely with product and business stakeholders.
- Demonstrated impact as a senior individual contributor on complex, cross-team initiatives.
Pay Range:
United States: 152,000 - 178,300 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
15Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
September 22, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.Pay Range:: 0 - 0 USD
Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:15
Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Long-term Incentive Reference Value Percentage:0 - 20
Long-term Incentive reference value is a market-based competitive value for your role.
See All 14 Machine Learning Manager Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Machine Learning Manager JobsMachine Learning Manager Jobs by City in Georgia
Where Georgia roles are concentrated, by current openings.
Machine Learning Manager Job Market in Georgia
A snapshot from current Georgia openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
What Georgia Employers Look For
The qualifications that appear most often in machine learning manager jobs across Georgia.
- Bachelor's or master's degree in computer science, statistics, or a related quantitative field
- Five or more years of hands-on machine learning engineering or data science experience
- Demonstrated experience leading and mentoring a team of data scientists or ML engineers
- Proficiency with Python, TensorFlow or PyTorch, and cloud ML platforms such as AWS SageMaker
- Experience managing end-to-end model lifecycle from research through production deployment
- Strong cross-functional communication skills for presenting model outcomes to business stakeholders
Machine Learning Manager Jobs in Georgia: Frequently Asked Questions
How do you become a machine learning manager in Georgia?
There is no state-issued license required to work as a machine learning manager in Georgia, so the path runs through education and experience. Most Georgia employers expect a bachelor's degree in computer science, mathematics, or a related field, with a master's degree increasingly preferred for leadership roles. Candidates typically build several years of hands-on ML engineering experience before moving into management, often within Georgia's fintech corridor in Atlanta or its growing defense-tech sector.
Which companies hire machine learning managers in Georgia?
Employers hiring machine learning managers in Georgia right now include The Coca-Cola Company, The Home Depot, and Speria, based on current listings on Migrate Mate as of September 2026. Georgia's concentration of financial services, logistics, and healthcare technology companies means demand is spread across both established Fortune 500 headquarters and fast-growing tech firms anchored in the Atlanta metro.
Which Georgia cities have the most machine learning manager jobs?
Atlanta have the most machine learning manager openings in Georgia. Atlanta drives the largest share due to its dense cluster of fintech firms, media companies, and corporate headquarters, while Alpharetta's technology corridor attracts enterprise software and cybersecurity employers, and Augusta's growing defense and healthcare sectors generate consistent ML leadership demand outside the main metro.
Are there remote machine learning manager jobs in Georgia?
Yes, and more than most fields. About 100% of machine learning manager openings tied to Georgia are remote or hybrid as of September 2026, reflecting how much of the work involves code, model review, and stakeholder presentations that transfer well to distributed settings. The parts of the role most likely to remain fully on-site are those tied to regulated data environments, such as healthcare or defense contracting positions.
How can I get hired as a machine learning manager in Georgia with little or no experience?
The most realistic entry path is moving laterally from a strong individual-contributor ML or data science role rather than applying directly to manager openings. Georgia employers like NCR Voyix, Cox Enterprises, and large Atlanta-area health systems regularly hire senior ML engineers with demonstrated project ownership, treating that as a pipeline for team lead and manager roles. Building a portfolio of deployed models, contributing to open-source ML projects, and earning a cloud ML certification strengthens a Georgia candidate's case for that first step into management.
Where can I find and apply to machine learning manager jobs in Georgia?
You can find and apply to machine learning manager jobs in Georgia on Migrate Mate, which lists current Georgia openings. Find the roles that fit your background and apply directly through each listing.
See All 14 Machine Learning Manager Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find Machine Learning Manager Jobs