Senior AI Data Scientist
📍 Location: Atlanta, GA,US (Hybrid)
🏢 Industry: Food and Beverage Manufacturing
💼 Work Setting: Hybrid
Are you passionate about leveraging Artificial Intelligence, machine learning, and advanced analytics to solve complex business challenges and drive operational excellence? Join an innovative, data-driven organization where you will develop cutting-edge AI solutions, transform large-scale industrial data into actionable insights, and help optimize critical business processes through advanced analytics and predictive technologies.
Key Responsibilities
AI & Machine Learning Solution Development
- Design, develop, deploy, and maintain machine learning, predictive analytics, and AI-driven solutions that address complex business and operational challenges.
- Build scalable models that improve efficiency, quality, forecasting, and decision-making across enterprise operations.
- Develop and operationalize machine learning solutions from proof of concept through production deployment.
- Evaluate emerging AI technologies and identify opportunities to create measurable business value.
Advanced Analytics & Data Science
- Analyze large, complex, and diverse datasets to uncover trends, patterns, risks, and opportunities.
- Develop statistical models, forecasting solutions, optimization algorithms, and predictive analytics capabilities.
- Transform complex analytical findings into actionable recommendations for business stakeholders.
- Perform exploratory data analysis to identify key drivers impacting operational and business performance.
Data Engineering & Model Deployment
- Prepare, cleanse, validate, and transform datasets to support model development and analytics initiatives.
- Build scalable data pipelines and support data integration efforts across multiple systems and platforms.
- Deploy and monitor machine learning models in production environments while ensuring performance, reliability, and scalability.
- Collaborate with engineering teams to integrate AI and analytics capabilities into enterprise applications and operational workflows.
Business Partnership & Stakeholder Collaboration
- Partner with business leaders, operational teams, engineers, and subject matter experts to understand challenges and define analytical solutions.
- Translate business requirements into data science strategies and technical approaches.
- Communicate analytical findings and model outcomes to both technical and non-technical audiences.
- Support strategic initiatives by providing data-driven recommendations and decision-support tools.
Innovation & Continuous Improvement
- Identify opportunities to improve operational performance through automation, optimization, and advanced analytics.
- Research and implement modern AI, machine learning, and data science methodologies.
- Drive innovation through experimentation with emerging technologies and advanced analytical techniques.
- Contribute to the development of best practices, standards, and governance for AI and analytics initiatives.
Data Governance & Model Management
- Ensure data quality, integrity, and consistency throughout the analytics lifecycle.
- Support responsible AI practices, model governance, and ethical use of data.
- Monitor model performance and implement continuous improvements to maintain accuracy and business relevance.
- Partner with governance and technology teams to ensure compliance with organizational standards and policies.
Required Qualifications
- Master's or Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Analytics, Operations Research, or a related quantitative field.
- Significant experience in data science, machine learning, artificial intelligence, advanced analytics, or predictive modeling roles.
- Strong expertise in statistical analysis, machine learning algorithms, and predictive modeling techniques.
- Experience working with large and complex datasets in enterprise environments.
- Strong problem-solving and analytical thinking skills.
- Ability to communicate complex technical concepts to diverse stakeholder groups.
Preferred Qualifications
- Advanced degree (Master's or PhD) in a quantitative or technical discipline.
- Experience deploying and maintaining machine learning models in production environments.
- Familiarity with cloud-based analytics and AI platforms.
- Experience developing optimization, forecasting, recommendation, or predictive maintenance solutions.
- Knowledge of MLOps, model lifecycle management, and AI governance frameworks.
- Experience working in manufacturing, supply chain, logistics, operations, industrial, or engineering-focused environments.
Technical Skills
- Python, R, SQL, or other analytical programming languages.
- Machine learning frameworks and data science libraries.
- Statistical analysis, predictive modeling, and optimization techniques.
- Data visualization and business intelligence tools.
- Big data platforms, cloud technologies, and data engineering concepts.
- Model deployment, monitoring, and MLOps practices.
- Feature engineering, data preparation, and experimentation methodologies.