Senior Data Scientist — ML/MLOps Lead (Hybrid, Temple)

Mclane Company, Inc.; Temple (TX); Hybrid; USD 120,000 - 150,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

FSA/HSA

Company-paid life insurance

Paid time off from day one

401(k) Profit Sharing after 90 days

Pet insurance

Maternity/paternity leave

Employee assistance programs

Discount programs

Tuition reimbursement

Job summary

Mclane Company, Inc. in Temple, TX is seeking a data science and ML leader to design, develop, and deploy advanced ML solutions that drive business value and efficiency.

You will own the end-to-end model lifecycle and provide technical leadership across ML/AI, MLOps, and DataOps. This hybrid role requires collaborating with cross-functional teams, mentoring junior data scientists, and deploying models in cloud environments using Python, Docker, GCP, and Azure, with a focus on accuracy,

Qualifications

• Bachelor's degree in Computer Science or related field or equivalent experience.
• 4+ years of experience working with Security Administration across Mainframe, Unix, and Windows environments.
• English proficiency sufficient to perform tasks and follow safety protocols.

Responsibilities

• Lead design, development, and deployment of machine learning and AI solutions.
• Own end-to-end model lifecycle from problem definition to deployment and monitoring.
• Build, test, and deploy production-grade models using Python and statistical methods.
• Implement MLOps and DataOps best practices, including CI/CD pipelines and governance.

Skills

Teamwork

Detail-oriented

Mentoring

Education

Bachelor's degree in Computer Science or related field

Tools

Python

GCP

Azure

Docker

Job description

Mclane Company, Inc. in Temple, TX is seeking a data science and ML leader to design, develop, and deploy advanced ML solutions that drive business value and efficiency.

You will own the end-to-end model lifecycle and provide technical leadership across ML/AI, MLOps, and DataOps. This hybrid role requires collaborating with cross-functional teams, mentoring junior data scientists, and deploying models in cloud environments using Python, Docker, GCP, and Azure, with a focus on accuracy,