Data Engineer

The opportunity

We are looking for a highly motivated and detail-oriented Data Scientist to join the Data Analytics team. The role supports a variety of business processes across engineering, technology, manufacturing, supply chain, and marketing & sales.

This position combines creativity, analytics, and business understanding to drive productivity and innovation. Working closely with engineers, analysts, and business stakeholders, the role contributes to the development of scalable analytical and machine learning solutions that deliver measurable business value.

The position offers the opportunity to work with large and complex datasets, advanced statistical techniques, predictive modeling, and modern cloud technologies. It also provides exposure to Microsoft Azure, Databricks, and data-driven initiatives that support business growth and operational excellence.

How you'll make an impact

• Lead the design, development, deployment, and improvement of machine learning models.
• Translate business challenges into scalable analytical and machine learning solutions.
• Analyze large and complex datasets to uncover trends and optimization opportunities.
• Apply statistical methods, hypothesis testing, and experimentation to solve business problems.
• Build, validate, and optimize models for classification, regression, clustering, and forecasting.
• Define model success criteria, evaluation metrics, and validation approaches.
• Collaborate with data engineering teams on data preparation, integration, and optimization.
• Present insights and recommendations to stakeholders and support less experienced team members.

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Strong hands-on experience with Python and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, and/or PyTorch.
• Strong proficiency in SQL and experience working with large relational datasets.
• Proven experience developing, validating, and deploying machine learning models in business environments.
• Strong foundation in statistics, probability, hypothesis testing, experimental design, and model evaluation.
• Experience with Power BI, Microsoft Azure, Azure Databricks, and cloud-based analytics or machine learning environments.