(Senior) Data Scientist

Continental is a leading tire manufacturer and industry specialist that develops and produces sustainable, safe and convenient solutions for automotive manufacturers as well as industrial and end customers worldwide. Founded in 1871, the company generated sales of €19.7 billion in 2025 and currently employs around 78,000 people in 54 countries and markets.

In Digital Solutions, we drive innovation for connected mobility and deliver cutting-edge digital products to our global fleet customers. As a Senior Data Scientist, you will be at the forefront of transforming data into intelligent, scalable solutions.

You will design, develop, and deploy advanced data science models that solve complex business challenges and deliver measurable impact. Working closely with Data Engineers, MLOps Engineers, and cross-functional teams, you will turn raw data into actionable insights and production-ready solutions that power smarter, safer, and more efficient mobility worldwide.

Responsibilities

Lead technical development projects, including scope definition, concept design, and proof-of-concept, and report progress to stakeholders,; Design and develop data science models to solve complex customer problems and conduct proof-of-concepts for feasibility and scalability,; Build and deploy validated models into production-ready solutions, ensuring robustness, maintainability, and lifecycle management,; Engage with business stakeholders to understand requirements, facilitate workshops, and translate technical topics into clear, non-technical language,; Collaborate with MLOps Engineer and Data Engineers to realize data-driven projects,; Mentor junior Data Scientists, review work, and organize knowledge-sharing sessions to foster best practices.

Requirements

Academic degree in Computer Science, IT, Engineering, Mathematics, or related field,; > 4 years of experience as a Data Scientist,; Reliable team player with structured self-organized workstyle,; Strong programming skills in Python and SQL,; Expertise in machine learning and AI (supervised, unsupervised, deep learning, time-series),; Proficiency in cloud and big data platforms,; Advanced skills in exploratory data analysis and dashboarding,; Hands-on experience with data engineering tools (e.g., Airflow),; Solid understanding of MLOps and model deployment practices,; Experience with Agile project management methods (Scrum, Kanban),; Fluent English language skills (written and spoken).

Benefits

Integration in a challenging and international work environment,; Flexible working model,; Agile and collaborative working style,; Continuous opportunities for the promotion of talent and training.