> Markdown version of [/jobs/ext/734887-data-scientist-machine-learning](https://www.wearedevelopers.com/jobs/ext/734887-data-scientist-machine-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Machine Learning - **Company:** QuEST Global - **Location:** Winston-Salem, NC, United States - **Salary:** $85,000.0 - $90,000.0 - **Contract:** Permanent contract - **Skills:** Agile Methodology, Amazon Web Services, Business Analytics Applications, Unit Testing, Cloud Computing, Databases, Data Warehousing, Software Design Patterns, DevOps, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Data Logging, Snowflake, Boto3, Containerization, Integration Tests, Kubernetes, Information Technology, Statistics Packages, Machine Learning Operations, Restful APIs, Software Version Control, Serverless Computing, Docker - **Published:** June 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3eef04c90a5e4d47 ## About the Role Do you have experience in Statistical analysis tools?, Do you have a Bachelor's degree?, * BS/MS in Computer Science/Data Science/Engineering or related field * Hands-on experience with ML frameworks, libraries, agile environments, and delivering machine learning solutions using DevOps principles. - Required. * Know-How in data science programming languages (Python, R, Scala) - Required. * Excellent knowledge of boto3 AWS SDK or additional SDKs for other cloud platforms and good knowledge of cloud infrastructure - Preferred. * Experience in container technologies (Docker, Kubernetes, etc.) - Preferred. * Familiar with REST API protocol and at least model serving technologies (MLFlow, Seldon Core, Kubeflow, TFX, Sagemaker endpoints, etc.) - Required * Excellent knowledge of the ML lifecycle - Preferred. * Ability in crafting CI/CD pipelines for machine learning projects - Required. * Experience with serverless computing (Python, Snowflake), knowledge of data warehousing systems, and knowledge of setting up target database systems. - Required. ## Description As a Data Scientist (Machine learning (ML) Ops) you will be part of our customers Digitalization journey, aligning with a broader team to work on challenging projects from all areas of the energy industry. Through combining strategic and business insights with the latest analytical techniques, you will drive maximum value from data and develop analytics solutions that provide strategic advantages in our day-to-day operations. You will transform Machine Learning models into well-engineered products fulfilling development, deployment and monitoring requirements and standards. * Deploy, operationalize, and maintain machine learning (ML) models with a focus on model hyperparameter optimization, automatic retraining and model training, version control and governance, and model monitoring and drift. * Establish workflows for model deployment, operations, and decommissioning. * Track, snapshot, and manage assets used to create models. * Collaboration, sharing, and standardization of ML pipelines developed by data scientists. * Maintain the integrity of model assets and logging access controls. * Certify model behavior to regulatory and adversary standards, supported by data scientists and subject matter experts. * Support portability of models across a variety of platforms. * Ensure model performance meets functionality and latency requirements. * Development, validation, and release of models. * Evaluate design patterns for model deployment, evaluate design patterns for unit testing and integration testing for machine learning products. * Build and maintain scalable ML Ops frameworks to support product-specific models. * Utilize skills and tools to support the WSC Digitalization Journey by building a network of connected machines and digital tools. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Best Coding Boot Camps in Germany](https://www.wearedevelopers.com/magazine/237-best-coding-boot-camps-in-germany) - [Got AI ideas but no money? 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