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Sports Data Science Internship Jobs in Decatur, GA

Minimum 2-4 years of professional experience in AI engineering, ML engineering, or applied data science (combination of internships and professional experience considered). * Demonstrated experience ...

As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues ... Own full-cycle recruiting for roles across Engineering, Data Engineering, Data Science, Data ...

Data & Analytics Consultant

Atlanta, GA · On-site

$89K - $148K/yr

Data Science Consulting Travel Required: Up to 10% Clearance Required: Ability to Obtain Public ... of internship or paid work thru college may be utilized as part of the 2 years). * Strong ...

Data Engineer

Atlanta, GA · On-site

$110.10K - $132.20K/yr

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field ... Prior experience in sports, entertainment, media, or live events industries * Familiarity with ...

OPB Fall 2026 Internship

Atlanta, GA · On-site

$17 - $22.25/hr

Under the direction of the Governor, develop fact-based, data-driven budget recommendations for the ... Graduate or undergraduate student in public administration, public policy, political science ...

Proven experience (3-5 years) as a Data Scientist or Machine Learning Engineer, with experience in ... Experience mentoring junior colleagues and interns.

Staff Machine Learning Engineer

Atlanta, GA · On-site

$220K - $280K/yr

... sports betting and daily fantasy ecosystems. What you'll do: * Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science ...

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

... sports betting and daily fantasy ecosystems. What you'll do: * Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science ...

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Sports Data Science Internship information

See Decatur, GA salary details

$11

$21

$41

How much do sports data science internship jobs pay per hour?

As of May 30, 2026, the average hourly pay for sports data science internship in Decatur, GA is $21.97, according to ZipRecruiter salary data. Most workers in this role earn between $16.88 and $23.94 per hour, depending on experience, location, and employer.

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

To thrive as a Sports Data Science Intern, strong quantitative skills, a background in statistics or data science, and experience with sports analytics are generally required. Familiarity with programming languages such as Python or R, data visualization tools, and knowledge of sports data platforms or databases is typical. Strong problem-solving abilities, attention to detail, and effective communication help interns stand out in team settings. These skills are crucial for analyzing complex sports data, generating actionable insights, and effectively conveying findings to coaches and decision-makers.

What types of projects or tasks can I expect to work on during a Sports Data Science Internship?

As a Sports Data Science Intern, you can expect to work on projects such as analyzing player performance data, building predictive models for game outcomes, and cleaning large datasets for use in analytics. Interns often collaborate with data scientists, coaches, and performance analysts to translate raw data into actionable insights. You'll likely use programming languages like Python or R and get exposure to sports-specific analytics software, providing valuable hands-on experience that can help launch your career in sports analytics.

What is a Sports Data Science Internship?

A Sports Data Science Internship is a temporary position that allows students or recent graduates to gain hands-on experience applying data science techniques to sports-related problems. Interns typically work with data from games, athletes, or teams to analyze performance, develop predictive models, and support decision-making in sports organizations. This role often involves using programming languages like Python or R, working with large datasets, and collaborating with coaches or analysts. Interns may also assist in visualizing data and presenting insights to help improve team strategies or player development.

What is the difference between Sports Data Science Internship vs Sports Data Analyst?

AspectSports Data Science InternshipSports Data Analyst
Required CredentialsRelevant coursework, basic programming skillsDegree in statistics, data science, or related field
Work EnvironmentInternship programs, sports teams, analytics companiesFull-time roles in sports organizations, analytics firms
Employer & Industry UsageEntry-level, training-focused positions in sports analyticsProfessional, ongoing data analysis roles in sports

The Sports Data Science Internship is an entry-level, training-focused position designed for students or recent graduates gaining experience in sports analytics. In contrast, a Sports Data Analyst is a full-time professional role requiring more experience and specialized skills. Internships provide foundational exposure, while analysts handle ongoing data projects in sports organizations.

What job categories do people searching Sports Data Science Internship jobs in Decatur, GA look for? The top searched job categories for Sports Data Science Internship jobs in Decatur, GA are:
What cities near Decatur, GA are hiring for Sports Data Science Internship jobs? Cities near Decatur, GA with the most Sports Data Science Internship job openings:
AI/ML Engineer

Full-time

Posted 25 days ago


Job description

IntelliTrans, (ITL), a subsidiary of Roper Technologies, Inc. (NYSE: ROP) is seeking an Applied AI Engineer to join our team, hybrid in Atlanta, GA.

Position Summary: The Applied AI Engineer a key member of the AI and Data Science Team, responsible for designing, developing, and deploying production AI/ML systems that power IntelliTrans’ intelligent freight management platform. This role blends applied AI engineering with data science fundamentals, including building and evaluating agentic AI systems, developing LLM-powered features, designing ML pipelines, and creating intelligent automation workflows. The ideal candidate is comfortable operating across the full spectrum from exploratory data analysis to production AI system deployment and is energized by applying AI to real-world logistics challenges.

Essential Duties and Responsibilities:

AI/ML Engineering and Agentic AI
  • Design, build, and evaluate agentic AI systems using Agentic AI platforms and related frameworks for freight management use cases (e.g., shipment exception handling, real time visibility, ETA intelligence).
  • Develop and optimize LLM-powered features, including prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, and tool-calling agents integrated into workflows
  • Build and maintain ML model evaluation frameworks with structured metrics, AI-assisted judges, and human feedback loops modern frameworks and DB Catalog tools
  • Contribute to developing browser automation + AI hybrid systems, including data extraction pipelines using Playwright and Claude.
  Data Science and Analytics
  • Analyze complex, large-scale freight and logistics datasets to generate actionable business insights for internal stakeholders and customers.
  • Develop and maintain predictive models for supply chain KPIs, including ETA prediction, freight audit anomaly detection, and shipment pattern analysis.
  • Support IntelliTrans’ data streaming strategy by designing data pipelines and feature engineering workflows that bridge the System of Record and System of Intelligence layers.
  • Build and iterate on dashboards and analytical tools using SQL, Analytics, and supporting visualization platforms.
Platform and Infrastructure
  • Develop production-grade Python services (FastAPI, async patterns) that integrate ML models and AI agents into the IntelliTrans platform.
  • Collaborate with the architecture team on AWS cloud infrastructure (ECS Fargate, SQS, S3, CloudWatch, Terraform) for model deployment and agent runtime scaling.
  • Contribute to CI/CD pipelines, observability instrumentation (OpenTelemetry, structlog), and MLOps best practices for model lifecycle management.
Collaboration and Strategy
  • Partner with Product Management to translate the AI agent roadmap into technical specifications and delivery plans aligned with IntelliTrans’ 3–5 year data and AI strategy.
  • Use modern Code Assistance tools such as Claude Code to write software.
  • Effectively handle multiple projects simultaneously in a deadline-driven environment.

 

QUALIFICATIONS AND BACKGROUND

 Education:       Bachelor’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, or related discipline required. Master’s degree in a quantitative field (Data Science, AI/ML, Engineering, Mathematics, or Statistics) preferred.

Experience:        Minimum 2–4 years of professional experience in AI engineering, ML engineering, or applied data science (combination of internships and professional experience considered).

  • Demonstrated experience building and deploying AI systems, ML models, or LLM-powered applications in a production environment.
  • Experience with modern data platforms, preferably Databricks (Delta Lake, MLflow, Unity Catalog) or equivalent (Snowflake, AWS SageMaker).
  • Experience in supply chain, logistics, freight management, or transportation technology is strongly preferred.
  • Hands-on experience with agentic AI concepts, LLM integration patterns, or RAG architecture is preferred.

Desired Skills:   

  • Strong proficiency in Python, including experience with FastAPI, pandas, scikit-learn, and async programming patterns.
  • Solid working knowledge of SQL and experience with relational databases (PostgreSQL preferred, Oracle experience a plus).
  • Experience with cloud platforms, primarily AWS (ECS, S3, SQS, Lambda, CloudWatch, Bedrock).
  • Familiarity with ML experiment tracking, model versioning, and MLOps workflows (MLflow preferred).
  • Proficiency with approaches to statistical analysis, mathematical modeling, and data visualization.
  • Experience creating and deploying AI Agents to production using Agentic Libraries and platforms
  • Knowledge of Infrastructure as Code (Terraform), containerization (Docker), and CI/CD pipelines (GitLab).
  • Experience with observability tools (OpenTelemetry, CloudWatch, structlog) for production ML systems.
  • Familiarity with document intelligence, and multimodal AI capabilities.

Soft Skills:

  • Strong analytical and problem-solving abilities with the capacity to operate across ambiguity.
  • Excellent communication skills, including the ability to present complex technical results to executive and non-technical audiences.
  • Self-directed learner comfortable rapidly adopting emerging AI/ML technologies and frameworks.
  • Collaborative mindset suited to cross-functional, agile team environments.
  • Intellectual curiosity about logistics, supply chain, and freight management domain problems.

Additional Information:

Location:

Atlanta, Georgia (Hybrid)

Reports To:

Data Science Manager

Team:

AI and Data Science Team (cross-functional: developers, architects, data scientists, scrum master, DBA)

Methodology:

Agile / Scrum with PI Planning cadence

Key Tools:

Databricks, Python, AWS, Claude API, FastAPI, Playwright, MLflow, Terraform, Docker, GitLab, SageMaker

 

 IntelliTrans supports workforce diversity and is a committed equal opportunity. / Affirmative action employer.