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

As a DataScientist, your role will be to evaluate and improve Scibids bidding algorithms, which are Scibids main asset, and to actively participate into Scibids automation and development process: each business case not yet automatically handled by our product should be met by the corresponding product improvement. This position will combine analytical rigor, a relentless curiosity, a strong knowledge of statistical methods and experience in implementing them.

This position will combine high development standards, a relentless curiosity, a strong understanding of business priorities and the ambition for your work to become a real game changer!

On a day to day basis, your responsibilities will include:

• working with massive datasets (billions of ads 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 team and help identify the business cases where a new algorithm is needed/existing ones need improvement.
• identify and automate 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 (e.g., statistics, probabilities, operations research, machine learning).
Work / academic projects experience in data analysis or related field (e.g., as a statistician/data scientist) with actual algorithms implementation in an industry programming language (eg Python, C++…)

Preferred qualifications:
Professional experience (up to 3 years) or PhD degree in data analysis (e.g., as a statistician/data scientist) including deep expertise and experience in applying mathematical methods to massive datasets.
Professional/academic experience in a SQL-like language.