D&H is looking to operationalize data science and AI models to drive insights, automation, and customer value by building end-to-end machine learning pipelines that scale across their new cloud-based data architecture.
Requirements
- 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.
- Experience integrating ML with SAP Datasphere/SAC and embedding outputs into BI dashboards.
- Familiarity with forecasting, anomaly detection, NLP, or recommender systems.
- Knowledge of DevOps/MLOps tooling (CI/CD pipelines, Docker, Kubernetes).
Responsibilities
- 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.
- 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.
- Establish model monitoring and drift detection to maintain accuracy and business relevance.
- Embed ML outputs into Tableau, Power BI, SAP Analytics Cloud (SAC), or API-driven integrations.
Other
- This is a fully remote role.
- 3+ years in machine learning engineering or applied ML roles.
- Collaborate with data engineers, analysts, and business stakeholders to translate requirements into ML solutions.
- Support citizen data science initiatives by building reusable frameworks and tools.
- Exposure to Netezza and Cognos legacy environments (for migration and feature replication).
