Senior Data Scientist
Who are we?
Skai (formerly Kenshoo) is a leading omnichannel marketing platform that leverages advanced AI and machine learning to deliver intelligent solutions based on data with performance media, enabling smarter decision-making, increased efficiency, and maximized returns – Revenue enabler for businesses around the world. Its partners include Google, Meta, Amazon, and Microsoft and more. ~$7 billion in ad spending is managed on the SkaiTM platform every year.
Established in 2006, We’re 700 employees strong. We work hybrid with a great home/ office work mix.
What will you do?
As a Senior Data Scientist at Skai, you will be a part of a large and innovative data science group, working closely with Dev teams and product managers, to create business value through Data Science and Machine Learning methods at the edge of technology, on diverse datasets.
The role consists of understanding customers' needs and product requirements, exploring data, identifying and defining functionality from conception to delivery, creating and improving algorithms and ML pipelines, building measurement tools, providing data visualizations, and more.
You'll deliver end-to-end ML products – including data processing, problem analysis, research, model development, validation and metrics, implement in production and online testing.
Requirements:
- M.Sc. in a quantitative discipline (e.g., Statistics, Bioinformatics, Computational Biology, Computer Science, Applied Mathematics or related fields) from a known university.
- Senior DS, 5+ years of significant hands-on data science experience, or PhD with extensive and relevant academic research experience.
- Strong programming skills in python. Ability to write clean and concise code.
- Strong skills in statistics & mathematics and experience with analyzing complex data
Strong communication skills, critical thinking, team player, fast learner.
- Advantage if you have experience with one or more of the following :
- Ad-tech industry experience.
- Time series modeling and analysis
- Deep Learning, using Tensorflow/Keras/pyTorch.
- Bayesian modeling/regression
- Reinforcement learning
- Comfortable diving into challenging, often open-ended problems
- Optimization: Markov decision process, linear programming
