Lead Data Scientist

About DV & Scibids

"DV Scibids" is a global leader in AI-powered digital campaign activation. Scibids' solutions empower global brands to effectively boost ad performance and outcomes while improving operational efficiency and reducing manual lift. DoubleVerify technology does not rely on digital identifiers such as cookies and can be activated across leading Demand-Side Platforms such as The Trade Desk, DV360, and Xandr to mention a few. To learn more visit https://doubleverify.com/scibids-ai/. DV Scibids' AI Optimisation technology equips these trading desks with machine learning solutions which use real-time performance data to discover the optimal amount to bid for each advertising opportunity, thus delivering the final advertisers significant lifts in performance and productivity. The technology developed by Scibids is rooted in a solid scientific and market expertise and now has a team of 30+ Data Scientists based in our Paris office, working alongside DV's global engineering and data science org.

About The Role

The mission of Scibids AI is to provide media buyers with fully automated buying strategies for display campaigns, leading to incomparable performances for a minimum of human work.

Responsibilities

As a Lead Data Scientist, you will help shape and strengthen the Scibids DoubleVerify bidding algorithms — the central intelligence behind our platform. You will drive the evolution of our automation framework, transforming unmet business cases into scalable product enhancements. This role blends rigorous statistical thinking with hands-on implementation, demanding both intellectual precision and practical execution. This position will combine high development standards, a relentless curiosity, a strong understanding of DV's business priorities and the ambition for your work to become a real game changer! On a day to day basis, your responsibilities will include:

• Managing projects end-to-end, taking ownership from the initial proof-of-concept phase through to production deployment and monitoring.
• Mentoring junior developers, providing technical guidance, conducting code reviews, and sharing best practices to ensure high code quality.
• Working with massive datasets (billions of ad impressions/month) and millions of variables.
• Solving non-routine analysis problems (not handled by off-the-shelf ML solvers).
• Solving challenging scaling/engineering problems as our system produces thousands of ML models per day.
• Working closely with the commercial and customer success teams to help identify business cases where a new algorithm is needed or existing ones need improvement.
• Identifying and automating any repetitive process taking place at Scibids, including cases where this means developing some AI routines replacing “human common sense.”

Minimum Qualifications

• MS degree in applied mathematics or data science (e.g., statistics, probabilities, operations research, machine learning).
• 4+ years of professional experience in Data Science with Python/C++.
• Experience deploying machine learning models into production environments.

Preferred Qualifications

• Proven professional experience in a SQL-like language.
• Previous experience leading technical projects or mentoring team members.
• Passion for automation and data-driven decisions.

Technical stack relevant to the position

• Python
• SQL
• Kubernetes, Docker, Github
• Google Cloud Environment, App Engine Standard, Cloud SQL, Google Cloud Datastore

Others

• Contract type: full-time position (French “CDI”).
• 3 days per week in the office
• Based in Paris (75008) – Saint Lazare
• Preferred availability: ASAP

The successful candidate’s base salary will be determined based on a number of non-discriminatory factors, including qualifications for the role, level, skills, experience, location, and internal equity considerations.

The estimated salary range for this role based on the qualifications set forth in the job description is between [€59,000 - €101,000]. This role may also be eligible for bonus or commission (as applicable), equity and benefits.