Senior Data Scientist / Staff Data Scientist at StackAdapt
Deutschland, , Germany -
Full Time


Start Date

Immediate

Expiry Date

11 Jul, 25

Salary

0.0

Posted On

11 Apr, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

StackAdapt is a self-serve advertising platform that specializes in multi-channel solutions including native, display, video, connected TV, audio, in-game, and digital out-of-home ads. We empower hundreds of digitally-focused companies to deliver outcomes and exceptional campaign performance everyday. StackAdapt was founded with a vision to be more than an advertising platform, it’s a hub of innovation, imagination and creativity.
We are searching for a talented senior level Data Scientist to join our engineering team as we continue to expand our data science efforts. Our platform is connected to thousands of publishers and advertisers worldwide and as a result, we’re dealing with millions of requests each second, making billions of decisions. We utilize the latest technologies to solve challenges in traffic, data storage, machine learning, and scalability.
Want to learn more about our Data Science Team: https://alldus.com/ie/blog/podcasts/aiinaction-ned-dimitrov-stackadapt/
Learn more about our team culture here: https://www.stackadapt.com/careers/data-science
Watch our talk at Amazon Tech Talks: https://www.youtube.com/watch?v=lRqu-a4gPuU
StackAdapt is a Remote First company, and we are open to candidates located anywhere in Germanyf for this position.

Responsibilities


    • Innovate ML algorithms to maximize ROI and advertising performance. This ranges from creating entirely new algorithms, to improvements on state-of-the art methods, to development using a deep understanding of classic methods

    • Write production code, sometimes collaborating with Data Engineers, to implement the novel ML algorithms
    • Prototype potential algorithms and pipelines, test them using historical data, and iterate to modify based on insights
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