Senior Data Scientist (Media) - CDI - H/F/X

As a Senior Data Scientist (Media) at Veepee, your key responsibilities will include leading data science projects, from defining the problem to deploying and maintaining machine learning models in production. You will also develop and refine algorithms for online advertising, focusing on multi-label classification and real-time bidding. Collaborating with your multidisciplinary team is essential, and you will play a crucial role in mentoring junior team members.

To be considered for the Senior Data Scientist (Media) role at Veepee, you should possess at least 5 years of experience in data science, along with a solid understanding of Python and SQL. Having experience with machine learning, especially with NLP using PyTorch, is crucial. It’s also important to demonstrate your analytical thinking, familiarity with statistics, and good development practices.

In the role of Senior Data Scientist at Veepee, you will engage with a diverse set of tools and technologies. Your work will primarily involve technologies such as Spark, Python, and Airflow for data processing, alongside AWS or GCP for cloud services. You will also utilize data visualization tools like Tableau and Microstrategy to present your findings effectively.

Absolutely! Veepee values continuous learning and offers various self-education courses available on our e-learning platform. Additionally, employees are encouraged to participate in meetups and international conferences, ensuring that you remain at the cutting edge of your field while also expanding your professional network.

Veepee promotes a dynamic and creative environment that thrives on collaboration and inclusion. As a Senior Data Scientist, you’ll find a work culture that celebrates diversity and values taking initiative. Team members are encouraged to express their individuality, share knowledge, and contribute to a supportive atmosphere where everyone can grow.

In the Senior Data Scientist (Media) role at Veepee, you will encounter various challenges, especially related to processing and managing large datasets in real-time, given our significant data volume (10TB/day). Strategies for effective automatic bidding and multi-label classification in a fast-paced environment will require innovative thinking and adaptability.

The recruitment process for the Senior Data Scientist role at Veepee typically involves a 30-minute initial HR screen, followed by a technical exchange with a Tech Lead and then an interview with a manager. This structured approach allows prospective candidates to showcase their technical skills and alignment with Veepee's values effectively.

When answering this question, focus on specific projects where you developed and deployed machine learning models. Discuss the algorithms you used, challenges you faced, and how you evaluated model performance. Highlight your approach to feature engineering and any innovative techniques you've employed.

Describe your approach to managing large datasets, particularly in a real-time bidding environment. Mention specific technologies you've used, like Spark for data processing, and explain how you ensure low latency while maintaining accuracy in bids.

Share your strategies for identifying and creating features that drive model performance. Discuss how you analyze and preprocess raw data, your techniques for dimensionality reduction, and any success stories where your feature engineering significantly improved outcomes.

Choose a project where you played a key role and that aligns with Veepee's objectives. Describe the project's goals, your contributions, the methodologies you used, and the final results, emphasizing your leadership and problem-solving skills.

Discuss your commitment to continuous learning through courses, attending conferences, reading the latest publications, or participating in online communities. Show your passion for staying updated with the latest trends and technologies in data science.

Explain the steps you take when deploying models, including testing, versioning, and documentation. Emphasize the importance of continuous monitoring and how you handle model updates to adapt to new data or market conditions.

Describe your approach to simplifying complex data insights into easy-to-understand narratives. Mention methods like data visualizations or storytelling techniques that you've used successfully, and how you tailor your message to suit different audiences.

Outline several best practices you follow, including code reviews, unit testing, and maintaining comprehensive documentation. Stress the importance of clean code and how it contributes to both collaboration and long-term project success.

Provide a brief background on your experience with SQL, highlighting key projects where you've optimized queries or managed large datasets. Discuss your familiarity with different database technologies and any challenges you've overcome in database management.

Emphasize that collaboration is essential in data science, particularly during model development and deployment. Share examples of how you’ve worked with cross-functional teams and contributed positively to the overall project success through sharing knowledge and offering support.