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Data Science Project Manager Jobs in Georgia (NOW HIRING)

Home Depot

Senior Data Scientist - MET

Atlanta, GA · On-site

You will serve as a lead on data science projects, collaborating with project/product managers, providing prioritization of tasks, balancing workload and mentoring data scientists on the team. This ...

Home Depot

DATA SCIENCE MANAGER

Atlanta, GA · On-site

This role leads overall programs, maximizing resources, partnering with project teams and business partners, and supports problem solving efforts. Data Science Managers provide leadership, mentoring ...

Peraton

Data Scientist SME/Project Manager

Augusta, GA · On-site

$176K - $282K/yr

This is a dual-hat leadership role requiring both deep technical expertise in data science and the ... Monitor and manage all phases of the project budget; coordinate with the government client on TDP ...

Peraton

Data Scientist / Project Manager

Augusta, GA · On-site

$146K - $234K/yr

This is a dual-hat leadership role requiring both deep technical expertise in data science and the ... Monitor and manage all phases of the project budget; coordinate with the government client on TDP ...

Home Depot

Senior Data Scientist, Online

Atlanta, GA · On-site

... science projects, collaborating with project/product managers, providing prioritization of tasks, balancing workload and mentoring data scientists on the project team. This role is expected to ...

Home Depot

Lead Data Scientist

Atlanta, GA · On-site

Based on the specific data science team, this role has expertise in one or more data science ... This position has 0 Direct Reports and leads/manages projects Travel Requirements: * Typically ...

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Data Science Project Manager information

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$14

$48

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How much do data science project manager jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for data science project manager in Georgia is $48.56, according to ZipRecruiter salary data. Most workers in this role earn between $42.02 and $56.83 per hour, depending on experience, location, and employer.

What is the hottest job of the 21st century?

Data Science Project Managers are in high demand due to the rapid growth of data-driven decision-making across industries. They oversee data projects, coordinate teams, and require skills in analytics tools, project management, and communication. The role is considered one of the most sought-after careers in the 21st century for its impact and earning potential.

What is a Data Science Project Manager?

A Data Science Project Manager is a professional who oversees and coordinates data science projects from inception to completion. They act as a bridge between technical data science teams and business stakeholders, ensuring that project goals align with organizational objectives. Responsibilities include planning project timelines, managing resources, mitigating risks, and communicating progress. They also help define project requirements, monitor deliverables, and ensure that outcomes meet quality standards. Strong communication, analytical, and organizational skills are essential for this role.

Is 40 too late for data science?

For a Data Science Project Manager, age is not a barrier to entering or advancing in the field. Success depends on skills, experience, and continuous learning, such as mastering tools like Python or R and understanding business needs, regardless of age.

Can data scientists make $300k?

Data scientists can earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and big data tools, and roles in high-paying industries or senior management positions. Achieving this level often requires a combination of technical expertise, certifications, and leadership responsibilities.

How does a Data Science Project Manager typically collaborate with data scientists and stakeholders throughout a project?

A Data Science Project Manager acts as a bridge between technical teams and business stakeholders, ensuring clear communication of goals, timelines, and deliverables. They facilitate regular meetings to discuss project progress, address any obstacles, and realign priorities as needed. By translating business requirements into actionable tasks for data scientists and providing updates to stakeholders, they help ensure that projects stay on track and deliver value. Effective collaboration often involves balancing technical feasibility with business needs, managing expectations, and fostering a cooperative team environment.

What is the difference between Data Science Project Manager vs Data Analyst?

AspectData Science Project ManagerData Analyst
Required CredentialsOften requires a bachelor’s or master’s in data science, analytics, or related fields; project management certifications beneficialTypically holds a bachelor’s degree in statistics, mathematics, or related areas; certifications like Microsoft Excel or Tableau are common
Work EnvironmentLeads data science projects, collaborates with data scientists, engineers, and stakeholdersAnalyzes data sets, creates reports, visualizations, and supports decision-making
Employer & Industry UsageUsed in tech, finance, healthcare, and consulting firms managing data science initiativesFound across industries for data reporting, business intelligence, and operational analysis

In summary, a Data Science Project Manager oversees data science projects and manages teams, requiring project management skills and relevant certifications. A Data Analyst focuses on analyzing data and creating reports, with a more technical and analytical role. Both roles are essential in data-driven organizations but differ in scope and responsibilities.

What are the key skills and qualifications needed to thrive as a Data Science Project Manager, and why are they important?

To thrive as a Data Science Project Manager, you need a solid understanding of data science methodologies, project management principles, and usually a degree in computer science, statistics, or a related field. Familiarity with analytics tools (such as Python, R, SQL), project management software (like Jira or Trello), and certifications such as PMP or Agile/Scrum are often required. Strong leadership, communication, and problem-solving skills set top performers apart by enabling effective team coordination and stakeholder management. These competencies ensure projects are delivered on time, within scope, and generate actionable insights that drive business value.

Can a data scientist become a project manager?

Yes, a data scientist can become a project manager by developing skills in leadership, communication, and project planning. Gaining experience in managing teams, understanding project workflows, and obtaining certifications like PMP can facilitate this transition.
What are popular job titles related to Data Science Project Manager jobs in Georgia? For Data Science Project Manager jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Data Science Project Manager jobs in Georgia look for? The top searched job categories for Data Science Project Manager jobs in Georgia are:
What cities in Georgia are hiring for Data Science Project Manager jobs? Cities in Georgia with the most Data Science Project Manager job openings:
Data Science Project Manager jobs near you
Senior Data Scientist - MET

Senior Data Scientist - MET

Home Depot

Atlanta, GA • On-site

Full-time

Posted 9 days ago

Home Depot rating

7.4

Company rating: 7.4 out of 10

Based on 6,349 frontline employees who took The Breakroom Quiz

6th of 39 rated national retailers

Job description

With a career at The Home Depot, you can be yourself and also be part of something bigger.
Position Purpose:
The Senior Data Scientist is responsible for leading data science initiatives that drive business profitability, process optimization, increased efficiencies, improve workflow automation using AI and improved store associate experience. This role focuses on building industry-leading Agentic AI capabilities and deploying Data Science models at scale for MET operations. Based on the specific data science project, this role would need to be knowledgeable in one or more data science specializations, such as optimization, computer vision, Multimodal AI, Conversational AI, Information Retrieval, Generative AI or search.
As a Senior Data Scientist, you are expected to seek out business opportunities to leverage data science as a competitive advantage. You will serve as a lead on data science projects, collaborating with project/product managers, providing prioritization of tasks, balancing workload and mentoring data scientists on the team. This role is expected to present insights and recommendations to leaders and business partners and explain the benefits and impacts of the recommended solutions. In addition, Data Scientists collaborate with business partners and cross-functional teams, requiring effective communication skills, building relationships and partnerships, and leveraging business acumen to solutions and recommendations.
Key Responsibilities:
  • 35% Solution Development - Proficiently design and develop algorithms and models to use against large datasets to create business insights; Execute tasks with high levels of efficiency and quality; Make appropriate selection, utilization and interpretation of advanced analytical methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Prepare reports, updates and/or presentations related to progress made on a project or solution; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
  • 30% Project Management & Team Support - Work with project teams and business partners to determine project goals; Provide direction on prioritization of work and ensure quality of work; Provide mentoring and coaching to more junior roles to support their technical competencies; Collaborate with managers and team in the distribution of workload and resources; Support recruiting and hiring efforts for the team
  • 20% Business Collaboration - Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide general education on advanced analytics to technical and non-technical business partners; Deep understanding of IT needs for the team to be successful in tackling business problems; Actively seek out new business opportunities to leverage data science as a competitive advantage
  • 15% Technical Exploration & Development - Seek further knowledge on key developments within data science, technical skill sets, and additional data sources; Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Define best practices and develop clear vision for data analysis and model productionalization; Contribute to library of reusable algorithms for future use, ensuring developed codes are documented

Direct Manager/Direct Reports:
  • This position reports to manager or above
  • This position has 0 Direct Reports

Travel Requirements:
  • Typically requires overnight travel less than 10% of the time.

Physical Requirements:
  • Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:
  • Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:
  • Must be eighteen years of age or older.
  • Must be legally permitted to work in the United States.

Preferred Qualifications:
  • Masters or PhD in a quantitative field (Computer Science, Math, Statistics, etc.)
  • Model Productionization: 4+ years of experience in deploying Data Science models from a development environment to a live application where it serves real-world predictions
  • AI Orchestration: 2+ years of experience in Data Science with a focus on Conversational AI, GenAI, Multimodal AI, agentic workflows, custom tool-calling
  • Technical Expertise: Must be proficient in a modern scripting language (preferably Python); Must be proficient running queries against data (preferably with Google BigQuery or SQL); proficient utilizing statistical techniques to identify key insights that help solve business problems; knowledgeable in Prescriptive Modeling like optimization, computer vision, recommendation, search or NLP; Demonstrated proficiency in predictive modeling, data mining, and data analysis

Minimum Education:
  • The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.

Minimum Years of Work Experience:
  • 5

Competencies:
  • Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs
  • Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
  • Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
  • Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
  • Cultivates Innovation: Creating new and better ways for the organization to be successful
  • Customer Focus: Building strong customer relationships and delivering customer-centric solutions
  • Develops Talent: Developing people to meet both their career goals and the organization's goals
  • Directs Work: Provides direction, delegating and removing obstacles to get work done
  • Drives Results: Consistently achieving results, even under tough circumstances
  • Nimble Learning: Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder
  • Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
  • Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels

What Home Depot employees say

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About Home Depot

Sourced by ZipRecruiter

The Home Depot is the world’s largest home improvement specialty retailer, operating a vast network of warehouse-format stores across the United States, Canada, and Mexico. Founded in 1978, the company has established itself as the primary resource for building materials, lawn and garden products, and home décor. Its business model caters to two distinct customer bases: Do-It-Yourself (DIY) homeowners and "Pro" customers, such as professional contractors and tradespeople. Beyond product sales, the company offers an extensive suite of services, including professional installation and one of the largest tool rental operations in North America.

Industry

Retail and manufacturing

Company size

10,000+ Employees

Headquarters location

Atlanta, GA, US

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