Data Scientist, Deep Learning (f/m/x)

As a Data Scientist Deep Learning at AUTO1 Group, you’ll be responsible for translating business needs into actionable data science questions. This includes the research and development of machine learning solutions that have a real positive impact on the business. You’ll partner closely with engineering and product teams to deploy these solutions, monitor model performance in production, and share actionable insights with stakeholders to drive growth.

To excel as a Data Scientist Deep Learning at AUTO1 Group, you should have at least 2 years of hands-on experience with deep learning and neural networks, with additional experience in recommender systems and PyTorch being a plus. Proficiency in SQL and Python, as well as familiarity with cloud platforms such as AWS, Azure, or Google Cloud, is important. A strong foundation in statistical analysis and the full data science project lifecycle will be essential.

At AUTO1 Group, Data Scientists enjoy a vibrant working environment in Berlin and the flexibility to work from home up to five days a week. We promote an open culture encouraging direct communication and collaboration among international teams. You'll engage in exciting team events such as Hackathons, enhancing both personal and professional growth, all while contributing your own ideas and creativity to impactful projects.

AUTO1 Group is committed to the continuous learning and development of its employees. Data Scientists, including those in the Deep Learning role, are provided with an educational budget to pursue further training or certifications. With opportunities to participate in innovation-driven events and collaboration with experienced colleagues, you’ll have plentiful chances to grow your skills and advance your career.

Data Scientists at AUTO1 Group utilize a variety of tools and technologies including SQL and Python for data manipulation and analysis. Familiarity with machine learning frameworks, particularly PyTorch, is beneficial, as is experience with cloud platforms like AWS, Azure, or Google Cloud. The role also involves using advanced statistical methods and visualization skills to present findings effectively.

When discussing your deep learning project, emphasize the objective, your role in the project, the technologies used, and the outcome. Make sure to highlight challenges faced, how you overcame them, and the impact of your work, especially on business KPIs.

Discuss techniques such as hyperparameter tuning, using dropout layers to prevent overfitting, and employing regularization methods. Mention experimenting with different network architectures and leveraging learning rate adjustments for optimization.

Common pitfalls include poor data quality and overfitting. To avoid these, ensure proper data cleaning protocols are in place, perform exploratory data analysis, and validate models using cross-validation techniques. Be proactive in monitoring model performance in production.

You can handle data imbalance by employing techniques like oversampling the minority class or undersampling the majority class, using SMOTE (Synthetic Minority Over-sampling Technique), or adjusting class weights in your algorithms to ensure that the model pays equal attention to all classes.

Mention tools such as Matplotlib, Seaborn, or Tableau. Discuss how these tools help to present data insights visually, allowing for easier interpretation and communication of complex results to stakeholders, which is essential in your role.

Express your methods for staying informed, such as following esteemed data science journals, attending conferences, participating in online forums, and completing relevant online courses. Emphasize the importance of continuous learning in keeping your skills sharp.

Highlight your ability to simplify complex findings by using visuals and relatable analogies. Describe how you prepare tailored presentations that focus on the implications of the results rather than the technical details when communicating with non-technical stakeholders.

Discuss your experience with the full lifecycle of data science projects. Include your familiarity with APIs, Docker, and cloud services for deployment, and how you tested and monitored deployed models to ensure they met performance expectations.

Identify key metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, explaining when and why you prefer using specific metrics based on the problem being solved, especially in imbalanced scenarios.

Detail your proficiency in SQL, explaining how you use it for querying databases, joining datasets, and performing aggregate functions. Highlight specific projects where data manipulation was crucial to model development, demonstrating your problem-solving skills.