We seek a key contributor to build and operate ML models and data science workflows that power analytics, optimization, and AI-driven use cases. In this role, you will design, implement, and deploy machine learning pipelines on Azure Databricks, leveraging Python and modern MLOps practices to turn data into production-grade AI solutions that support business processes and data-driven decision-making across the organization.

You might be our missing piece if you have:

• A completed degree in Computer Science, Business Informatics, Industrial Engineering, or a comparable engineering or natural sciences field

• Professional experience in the design and implementation of data/ML pipelines and production ML environments

• Hands-on experience with Azure Databricks (2+ years in production environments), including: MLflow integration; Databricks Model Registry and model serving, feature Store

• Experience deploying ML models at scale

• A solid understanding of business domains and processes within a large enterprise, with the ability to translate business needs into data-driven solutions

• Experience working in interdisciplinary teams and collaborating effectively across functions

• Strong knowledge of Python and the Python data science ecosystem (e.g., pandas, NumPy, scikit-learn, PySpark)

• A solid foundation in statistics, machine learning, feature engineering, and model evaluation

• Data engineering awareness: stream/batch processing, data quality, Medallion Architecture, and orchestration

We would be thrilled if you have:

• Familiarity with Docker and Kubernetes

• Knowledge of Infrastructure as Code (IaC) concepts and tools (e.g., Terraform) for automated and reproducible infrastructure provisioning

• Familiarity with Grafana and Power BI

• A proactive mindset toward continuous improvement and optimization in ML/data science practices

• A strong sense of ownership and accountability in delivering high-quality, production-ready models

• A collaborative attitude and enthusiasm for working in dynamic, cross-functional environments

• A sense of belonging while reading about our culture

We will be working together on:

• Building and operating ML models and data science workflows that support analytics, optimization, and AI-driven use cases

• Designing, developing, and deploying scalable ML pipelines on Azure Databricks

• Managing the full model lifecycle using MLflow, Databricks Model Registry, and Feature Store

• Deploying and monitoring ML models at scale in production environments

• Collaborating with interdisciplinary teams to translate business needs into data-driven, AI-powered solutions

• Continuously improving data science and MLOps practices, tooling, and processes to support innovation and scalability