Lead Data Scientist
About The Position
Tyson Foods, Inc. has an opening for Lead Data Scientist in Springdale, AR. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Responsible for end-to-end testing, including deployment smoke tests and continuous monitoring. Co-own machine learning operations (MLOps) roadmap, support and mentor teams in achieving milestones, showing leadership in artificial intelligence (AI) strategy and the practical application of model evaluation and validation. Responsible for scaling and cost-efficient model deployment, ensuring responsiveness and observability. Execute complex software testing practices, including end-to-end, integration, and smoke testing in machine learning pipelines. Scale model deployment with an emphasis on performance, cost, and observability, including model monitoring at appropriate cadences. Broaden data science lifecycle management, involving roadmaps with business outcomes verified by business and finance teams. Contribute to the design of reusable machine learning architecture assets and patterns.
Requirements
- Bachelor’s degree or foreign equivalent in Computer Science, Mathematics, Statistics, or a related field, and 3 years of experience in the job offered or related occupation.
- Master’s degree or foreign equivalent in Computer Science, Mathematics, Statistics, or a related field and 2 years of experience in the job offered or related occupation.
- At least 2 years of experience in Machine Learning, including binary classifiers using Scikit-Learn and TensorFlow.
- At least 2 years of experience in Data Engineering, including managing unstructured data ingestion, and staging.
- At least 2 years of experience in Designing and deploying scalable RESTful web services to expose machine learning models to production.
- At least 2 years of experience in Computer Vision, including applied advanced image processing techniques and object segmentation.
- At least 2 years of experience in Distributed Systems, including architecting message queuing systems to handle asynchronous task processing.
- At least 2 years of experience in Deployment and Version Control, including utilizing containerization and version control for software lifecycle management using Docker and Git.
Responsibilities
- Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software.
- Responsible for end-to-end testing, including deployment smoke tests and continuous monitoring.
- Co-own machine learning operations (MLOps) roadmap, support and mentor teams in achieving milestones, showing leadership in artificial intelligence (AI) strategy and the practical application of model evaluation and validation.
- Responsible for scaling and cost-efficient model deployment, ensuring responsiveness and observability.
- Execute complex software testing practices, including end-to-end, integration, and smoke testing in machine learning pipelines.
- Scale model deployment with an emphasis on performance, cost, and observability, including model monitoring at appropriate cadences.
- Broaden data science lifecycle management, involving roadmaps with business outcomes verified by business and finance teams.
- Contribute to the design of reusable machine learning architecture assets and patterns.
Benefits
- paid time off
- 401(k) plans
- affordable health, life, dental, vision and prescription drug benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
