D and H Distributing Co
Machine Learning Engineer
This is a fully remote role.
D&H is growing!
Join 100+ year old Employee-Owned technology distributor, offering end-to-end solutions for today's resellers, retailers, and the clients they serve across the SMB and Consumer markets. We are empowered by our employee Co-Owners who provide the industry's best service, and we promote a collaborative culture. We offer an Employee Stock Ownership Plan, 401k, Paid Time Off, Medical, Prescription, Dental and Vision benefits as well as Gym Reimbursement, Work from Home Reimbursement, Employee Purchase Program, Tuition Assistance and much more! As a D&H Co-Owner you receive numerous discounts on services. We feel strongly about giving back to the community and promoting sustainable, eco-friendly business practices. SUMMARY
The Machine Learning Engineer will operationalize data science and AI models that drive insights, automation, and customer value. The ML Engineer will play a key role in building end-to-end machine learning pipelines that scale across D&H's new cloud-based data architecture.
This role bridges data science research and enterprise-scale deployment, ensuring ML models are production-ready, monitored, and tightly integrated into business processes. You'll work closely with Finance Managers, data engineers, and analysts to deliver solutions that enhance predictive analytics, personalization, forecasting, and automation.
The DSI team is building a modern analytics environment that blends data science, AI, and business intelligence. By joining, you'll:
Help productionize ML models across sales forecasting, customer segmentation, inventory optimization, and automation use cases. Operate in a hybrid environment bridging Netezza/Cognos legacy with Azure, Databricks, and SAP S/4HANA future state. Collaborate with a team passionate about continuous improvement, innovation, and applied analytics. Contribute to D&H's customer and partner-facing analytics strategy by embedding AI into reporting and insights delivery. ESSENTIAL DUTIES AND RESPONSIBILITIES
ML Engineering & Deployment
• Build, deploy, and monitor ML models using Databricks MLflow, and Python frameworks (scikit-learn, TensorFlow, PyTorch).
• Automate model training, retraining, and deployment pipelines integrated with Databricks Delta Lake and Azure Data Lake.
• Implement CI/CD for ML models to ensure reliable and scalable deployment.
Data Pipeline Integration
• Partner with data engineers to ensure clean, curated, and feature-rich datasets are available for ML training.
• Develop feature engineering pipelines that leverage Azure Synapse, Databricks, and SAP Datasphere.
• Optimize data retrieval and transformation for real-time or batch ML use cases.
Monitoring & Governance
• Establish model monitoring and drift detection to maintain accuracy and business relevance.
• Document ML processes and maintain compliance with data governance and security standards (Azure Purview, Unity Catalog).
• Ensure alignment with D&H's enterprise architecture and SAP S/4HANA integration strategy.
Collaboration & Business Impact
• Collaborate with analysts and business stakeholders to translate requirements into ML solutions.
• Embed ML outputs into Tableau, Power BI, SAP Analytics Cloud (SAC), or API-driven integrations.
• Support citizen data science initiatives by building reusable frameworks and tools.
EDUCATION and/or EXPERIENCE Education
Bachelor's degree in Computer Science, Data Science, Engineering, or related field. Experience
3+ years in machine learning engineering or applied ML roles. Proficiency in Python, SQL, PySpark and ML libraries (scikit-learn, TensorFlow, PyTorch). Hands-on experience with Databricks MLflow and/or Azure ML pipelines. Strong understanding of data modeling, feature engineering, and ML lifecycle management. Knowledge of cloud architectures (Azure, Databricks, Snowflake) and modern data engineering practices. Preferred
Master's degree in Data Science, AI, or related field. Experience integrating ML with SAP Datasphere/SAC and embedding outputs into BI dashboards. Familiarity with forecasting, anomaly detection, NLP, or recommender systems. Exposure to Netezza and Cognos legacy environments (for migration and feature replication). Knowledge of DevOps/MLOps tooling (CI/CD pipelines, Docker, Kubernetes).
This is a fully remote role.
D&H is growing!
Join 100+ year old Employee-Owned technology distributor, offering end-to-end solutions for today's resellers, retailers, and the clients they serve across the SMB and Consumer markets. We are empowered by our employee Co-Owners who provide the industry's best service, and we promote a collaborative culture. We offer an Employee Stock Ownership Plan, 401k, Paid Time Off, Medical, Prescription, Dental and Vision benefits as well as Gym Reimbursement, Work from Home Reimbursement, Employee Purchase Program, Tuition Assistance and much more! As a D&H Co-Owner you receive numerous discounts on services. We feel strongly about giving back to the community and promoting sustainable, eco-friendly business practices. SUMMARY
The Machine Learning Engineer will operationalize data science and AI models that drive insights, automation, and customer value. The ML Engineer will play a key role in building end-to-end machine learning pipelines that scale across D&H's new cloud-based data architecture.
This role bridges data science research and enterprise-scale deployment, ensuring ML models are production-ready, monitored, and tightly integrated into business processes. You'll work closely with Finance Managers, data engineers, and analysts to deliver solutions that enhance predictive analytics, personalization, forecasting, and automation.
The DSI team is building a modern analytics environment that blends data science, AI, and business intelligence. By joining, you'll:
Help productionize ML models across sales forecasting, customer segmentation, inventory optimization, and automation use cases. Operate in a hybrid environment bridging Netezza/Cognos legacy with Azure, Databricks, and SAP S/4HANA future state. Collaborate with a team passionate about continuous improvement, innovation, and applied analytics. Contribute to D&H's customer and partner-facing analytics strategy by embedding AI into reporting and insights delivery. ESSENTIAL DUTIES AND RESPONSIBILITIES
ML Engineering & Deployment
• Build, deploy, and monitor ML models using Databricks MLflow, and Python frameworks (scikit-learn, TensorFlow, PyTorch).
• Automate model training, retraining, and deployment pipelines integrated with Databricks Delta Lake and Azure Data Lake.
• Implement CI/CD for ML models to ensure reliable and scalable deployment.
Data Pipeline Integration
• Partner with data engineers to ensure clean, curated, and feature-rich datasets are available for ML training.
• Develop feature engineering pipelines that leverage Azure Synapse, Databricks, and SAP Datasphere.
• Optimize data retrieval and transformation for real-time or batch ML use cases.
Monitoring & Governance
• Establish model monitoring and drift detection to maintain accuracy and business relevance.
• Document ML processes and maintain compliance with data governance and security standards (Azure Purview, Unity Catalog).
• Ensure alignment with D&H's enterprise architecture and SAP S/4HANA integration strategy.
Collaboration & Business Impact
• Collaborate with analysts and business stakeholders to translate requirements into ML solutions.
• Embed ML outputs into Tableau, Power BI, SAP Analytics Cloud (SAC), or API-driven integrations.
• Support citizen data science initiatives by building reusable frameworks and tools.
EDUCATION and/or EXPERIENCE Education
Bachelor's degree in Computer Science, Data Science, Engineering, or related field. Experience
3+ years in machine learning engineering or applied ML roles. Proficiency in Python, SQL, PySpark and ML libraries (scikit-learn, TensorFlow, PyTorch). Hands-on experience with Databricks MLflow and/or Azure ML pipelines. Strong understanding of data modeling, feature engineering, and ML lifecycle management. Knowledge of cloud architectures (Azure, Databricks, Snowflake) and modern data engineering practices. Preferred
Master's degree in Data Science, AI, or related field. Experience integrating ML with SAP Datasphere/SAC and embedding outputs into BI dashboards. Familiarity with forecasting, anomaly detection, NLP, or recommender systems. Exposure to Netezza and Cognos legacy environments (for migration and feature replication). Knowledge of DevOps/MLOps tooling (CI/CD pipelines, Docker, Kubernetes).