Data Scientist / Machine Learning Engineer

Your Role

As a Data Scientist / Machine Learning Engineer, you will design, build and deploy machine learning and data science solutions in an enterprise environment. You will work closely with cross-functional teams to turn complex data into actionable insights and integrate models into existing business processes and IT infrastructures.

Your Responsibilities

• Design, develop and implement machine learning and data science solutions in an enterprise environment.
• Prepare and analyse complex datasets from various sources, including handling missing and incorrect values.
• Apply and optimise machine learning methods, including linear regression, random forest, K-nearest neighbors, neural networks, gradient boosting, clustering methods, reinforcement learning, and decision/regression trees.
• Perform feature engineering and hyperparameter tuning.
• Build models using Python and/or R and standard data-science libraries.
• Use cloud platforms such as Azure ML and AWS SageMaker, and contribute to automated ML pipelines and MLOps practices.
• Collaborate with cross-functional teams to integrate ML solutions into business processes and IT systems.

Your Profile

• Solid experience designing, developing and implementing ML and data science solutions in an enterprise environment.
• In-depth knowledge of statistics and data analysis, including confidence intervals, significance tests and survival analyses.
• Strong programming skills in Python; R is a plus.
• Experience with cloud platforms such as AWS SageMaker or Azure ML; knowledge of ML pipelines and MLOps is desirable.
• Team-oriented, analytical and solution-oriented approach, with strong communication skills.
• Experience in GenAI is preferred.
• Fluent English; German is a plus.

Benefits

• B2B or employment contract.
• Working with latest technologies.
• Industry-leading healthcare and comprehensive medical coverage (Luxmed).
• Opportunities to enhance your qualifications.
• Customizable one-to-one English or German lessons.
• Modern computer equipment.
• Opportunities to network and connect through get-togethers and team-building events.
• Employee referral programme.
• Flexible working schedule.
• 100% remote-work flexibility.