Senior Data Scientist in Ad Tech

As a Senior Data Scientist at Teads, you will oversee large-scale distributed machine learning systems, focusing on models for Click-Through Rate (CTR) and Conversion Rate (CVR), among other complex tasks. Your role will require you to collaborate with machine learning engineers and product managers, identify investment opportunities, and lead various projects from conception to production. This position also emphasizes staying updated with industry standards and researching innovative solutions to optimize advertising outcomes.

To qualify as a Senior Data Scientist at Teads, candidates should possess deep experience in the ad tech industry, complemented by strong skills in statistics and artificial intelligence. Familiarity with machine learning, data mining, and the ability to critically analyze scientific literature are essential. Additionally, strong communication and problem-solving skills are crucial for effective collaboration within diverse, cross-functional teams.

At Teads, the AI department comprises around 50 professionals, including data scientists, machine learning engineers, and backend engineers. This diverse team works collaboratively to drive advertising solutions and ensures the delivery of sophisticated technologies for effective campaign outcomes. The environment is supportive, aiming for optimum performance while promoting work-life balance, which is essential for productivity and creativity.

Teads promotes a vibrant and inclusive work culture that prioritizes communication, teamwork, and open-mindedness. As a Senior Data Scientist, you'll find yourself in a dynamic and diverse environment where every voice counts and everyone has a seat at the table. Initiatives such as Employee Resource Groups empower employees to discuss various topics that drive change, ensuring you feel valued and supported in your pursuits both inside and outside of work.

In the role of a Senior Data Scientist at Teads, you'll engage in a variety of projects geared towards enhancing our advertising solutions. You’ll explore online learning regimes, cold start problems, embeddings for users and ads, and ad matching systems. Your role will include advising teams, implementing solutions, experimenting with new methods, and occasionally publishing findings at conferences. Each project is designed to provide maximum economic impact while supporting your professional growth.

Highlight specific machine learning projects you've worked on in ad tech, discussing the challenges faced, technologies used, and results achieved. Emphasize how these experiences prepared you for the complexities of ad delivery systems.

Discuss your methods for keeping abreast of industry changes, such as reading scientific journals, attending conferences, and participating in webinars. Mention any networking groups or online communities that help you remain engaged with the latest developments.

Share a specific example, detailing the problem, the data collection process, the model selection, and the implementation steps. Highlight your role in collaboration with other team members and the measurable impact of the solution.

Be ready to outline specific challenges such as data quality issues or algorithm inefficiencies. Discuss the steps you took to address these challenges, focusing on critical thinking and problem-solving skills.

Demonstrate your ability to break down complex ideas by using plain language and relatable analogies. Discuss your experience in training or communicating with non-technical teams and how you ensure that everyone understands the implications of the data work.

Emphasize the importance of statistical analysis in validating models and making data-driven decisions. Share examples of how regression analysis or hypothesis testing provided insights that influenced project direction.

Share your enthusiasm for Teads’s mission and culture. Highlight how your skills align with the company’s objectives in leveraging AI and data science to create impactful advertising solutions in a vibrant and diverse setting.

Talk about the tools you've utilized for project management and team collaboration, like Git, JIRA, or Slack. Emphasize your experience working within cross-functional teams and how these tools supported effective communication and progress tracking.

Discuss your methods for project management, such as defining key priorities, setting timelines, and using agile methodologies. Share any tools or frameworks you use to track progress and communicate with your team about project statuses.

Outline your approaches to feature selection, including techniques like correlation analysis, recursive feature elimination, and using domain knowledge. Mention how these techniques help improve model accuracy and efficiency.