Senior Data Scientist

We are looking for a Data Scientist to join our R&D team. To become part of our journey in helping digital publishers and businesses improve their business growth, empowering meaningful interactions across their digital assets, generating an undeniable increase in revenue, user engagement, conversions, and overall performance.
Build the next generation of ML algorithms for us. Focusing primarily on recommendation engines, personalization, pricing, prediction and causal inference models.
Our Story:
At the heart of every digital revolution, there's a story. Ours began in 2012, with a vision of creating a publisher video platform that offers all the necessary tools, innovation, and knowledge publishers need to fully optimize their websites with video.
Through machine-learning and AI technology, we empower publishers to own their video content and monetization strategies to maximize revenue. Our platform offers global publishers unparalleled access to best-in-class ad serving, monetization, content automation, and video recommendation.
Trusted by some of the leading publishers globally including The Arena Group, CBSi, Hearst, Nasdaq, VICE, and more, our platform helps publishers boost audience engagement and increase revenue. Our success is a direct result of our dedicated team located around the world and visionary backers including The Walt Disney Company, Saban Ventures, Viola Group, 83North, and Firstime Ventures.
At our core is a passion for reimagining the video space and an unyielding commitment to those who rely on us. And now, we're looking for remarkable talent like you to join the next chapter of our story.
Long story short, you will:
Design, build, automate, deploy and maintain machine learning production pipelines, utilizing cloud technologies such as cloud AI / Sagemaker.
Building RAG processes and utilizing LLMs for verius needs.
Deploy and monitor models in production (e.g. building artifacts, measuring model drift, etc.).
Implement and evaluate recommender engines and deep-learning techniques, to work in a production environment and run at massive scale.
Apply complex analytical techniques to derive actionable insights with very good communication skills to write and publish internal documentation to be used by technical and business teams.
Design and execute technical processes needed for experimentation to support an array of business problems, including experiment ideation, experimental design, monitoring, data analysis, code review and communication of results.
Keep up with machine learning research and commercial product offerings across a wide variety of machine learning fields
Role Key Deliverables: Own current recommendation engine modeling stack and its development processes (creation/specification of tasks, daily updates and commitment to deliverables), research and deploy incremental improvements.