Senior Data Scientist (f/m/x)

As a Senior Data Scientist at AUTO1 Group, you'll be tasked with translating business needs into data science questions, conducting research and development of machine learning solutions, and partnering with engineering and product teams to deploy these solutions into our tech infrastructure. You'll also monitor model performance in production, share insights with key stakeholders, and identify growth opportunities.

The ideal candidate for the Senior Data Scientist role at AUTO1 Group should have at least two years of hands-on experience with deep learning and neural networks, as well as proficiency in SQL and Python. Familiarity with AWS, Azure, or Google Cloud would be advantageous. You should also have a strong background in the full lifecycle of data science projects, and excellent stakeholder interaction skills.

At AUTO1 Group, Senior Data Scientists primarily work with technologies like Python and SQL for data manipulation and modeling. Experience with deep learning frameworks, particularly PyTorch, is highly desired, along with knowledge of cloud platforms such as AWS, Azure, or Google Cloud.

AUTO1 Group is committed to the continuous learning and development of its employees, including Senior Data Scientists. The company provides an educational budget to support professional growth, along with exciting team events and opportunities for collaboration and innovation, such as Hackathons.

Yes! At AUTO1 Group, you can enjoy the flexibility of working from home up to five days a week while being part of an international team committed to innovation and success.

When answering this question, highlight specific projects where you've implemented machine learning models, the technologies you used, and the resulting impact. Mention any particular challenges you faced and how you overcame them to demonstrate your problem-solving skills.

Discuss your approach to identifying key business objectives and how you then derive specific data science inquiries from them. Provide examples of times you've successfully aligned technical solutions with business goals in prior roles.

Explain the importance of performance metrics and validation processes. Discuss specific techniques you've employed to assess model accuracy, precision, and recall, ensuring they meet business standards and expectations.

Describe your experience collaborating with engineering and product teams to integrate data science solutions into production. Include examples of your communication strategies to ensure project alignment and success.

Illustrate a specific instance where your data analysis revealed insights that led to a strategic business decision. Highlight how you approached the analysis, the data sets you used, and the outcome.

Mention your competencies in languages like Python and SQL, as well as any frameworks or libraries such as TensorFlow or PyTorch you’ve used. Discuss how you've applied these tools to real-world data science projects.

Showcase your problem-solving approach in the face of uncertainty. Discuss techniques like exploratory data analysis to clarify your understanding of the data and iteratively refine your analysis based on emerging patterns.

Emphasize the importance of teamwork and communication. Discuss how you've successfully collaborated with cross-functional teams and how these interactions enhance project outcomes.

Share your knowledge and hands-on experience with cloud services, detailing any specific projects where cloud technology facilitated data processing, model training, or deployment.

Discuss your commitment to continuous learning in data science, including attending workshops, enrolling in courses, or following relevant publications and conferences to keep your skills sharp and current.