Senior Specialist - Data Sciences

SUMMARY

We are seeking a Machine Learning Engineer to join the Information Technology organization at our corporate office in Springfield MO

The Machine Learning Engineer designs builds tests and optimizes machine learning systems that support enterprise AI initiatives across the business This role is responsible for developing productionready model code inference logic and reusable ML components that convert approved enterprise data into reliable operational signals recommendations automations or insights

This position works closely with AI leadership Data Science MLOps Data Engineering ProductDelivery Security Privacy Store Operations Merchandising and other crossfunctional partners to implement practical AI solutions The role must balance model quality latency cost privacy maintainability and operational usefulness

ESSENTIAL FUNCTIONS

Design develop and evaluate machine learning models and inference pipelines for enterprise AI use cases across retail operations merchandising customer experience supply chain and corporate functions

Build productionquality Python code for model training evaluation preprocessing postprocessing inference services and reusable model components

Partner with Data Scientists to define ground truth datasets labeling requirements evaluation metrics confidence thresholds and acceptable error tradeoffs

Partner with MLOps Cloud ML Engineering to package register deploy monitor and optimize models in cloud edge or hybrid environments

Evaluate and select model architectures pretrained models finetuning approaches and inference strategies appropriate for the business problem and operating environment

Prepare and transform approved structured and unstructured data for model development while following privacy retention and acceptableuse constraints

Build or integrate data labeling sampling augmentation and validation workflows needed for model development and evaluation

Optimize inference performance for latency cost throughput reliability and deployment target

Implement model output schemas and event metadata structures in partnership with Data Engineering and APIapplication teams

Integrate model outputs with APIs event streams dashboards reports applications or other approved enterprise presentation layers

Write automated tests for model code preprocessing logic inference services schema contracts and regression checks

Troubleshoot model failures caused by data quality domain shift operational changes drift or degraded source data

Document model assumptions limitations dependencies reproducibility steps evaluation results and production readiness criteria

Support responsible AI practices including PII minimization privacyaware design model explainability where practical and secure handling of approved enterprise data

Contribute to architecture decision records model cards technical runbooks documentation and reusable engineering standards

ALL OTHER DUTIES AS ASSIGNED

EXPERIENCE QUALIFICATIONS

Minimum Degree Required Bachelors Degree in Computer Science Artificial Intelligence Machine Learning Data Science Electrical Engineering Computer Engineering Applied Mathematics or a related technical field or equivalent experience

5 years of experience in software engineering machine learning engineering applied AI engineering or production ML systems

3 years of handson experience building training finetuning or deploying machine learning models in applied business environments

Strong proficiency in Python and modern machine learning frameworks such as PyTorch TensorFlow scikitlearn OpenCV or equivalent tools

Experience with one or more ML domains such as natural language processing forecasting classification recommendation systems optimization anomaly detection multimodal AI or generative AI

Experience building productionquality APIs services or batchstreaming inference components

Experience with Git automated testing code review containerization and collaborative engineering practices

Familiarity with model optimization and deployment formats or tooling such as ONNX TensorRT OpenVINO quantization batching or similar techniques preferred

Familiarity with Azure Machine Learning Azure AI services Databricks MLflow or equivalent cloud ML platforms preferred

Familiarity with eventdriven architectures REST APIs message queues data lakes and metadataevent pipelines preferred

Experience with distributed inference realtime AI systems highthroughput event processing or enterprise integration patterns preferred

Experience working with security privacy and governance requirements for sensitive operational data preferred

KNOWLEDGE SKILLS AND ABILITY

Strong software engineering fundamentals and ability to build maintainable ML systems beyond notebooks