Description

As a Principal Data Scientist in AdTech, you will lead the development of innovative AI models that enhance digital advertising. Your work will focus on optimizing auction dynamics and maximizing ROI for advertisers, while also shaping the future of data-driven decision-making.

Requirements

• •Expertise in ranking models and personalisation
• •Ability to invent and adapt novel ML architectures
• •Deep operational knowledge of AI frameworks
• •Understanding of ad auctions and ad exchange platforms
• •Track record of using probabilistic modelling and causal inference
• •Experience optimising key business metrics
• •Advanced proficiency in Python and SQL
• •Foundation in probabilistic modelling and experimental design
• •Track record of designing and deploying large-scale AI systems
• •Experience defining technical strategy for Data Science and ML
• •History of mentoring engineers and building alignment

Tasks

• •Drive innovation in AI-powered ranking and bidding systems
• •Lead the development of advanced models and strategies for auction dynamics
• •Maximize advertiser ROI through exceptional user experiences
• •Elevate advertising intelligence capabilities and set industry benchmarks
• •Invent and adapt novel ML architectures for complex problems
• •Research and implement state-of-the-art advancements in relevance and ranking
• •Evaluate and integrate cutting-edge solutions into the technology stack
• •Apply AI frameworks like TensorFlow and PyTorch for real-time ranking systems
• •Understand ad auctions and macro-economic dynamics in programmatic advertising
• •Use probabilistic modeling and causal inference to inform auction design
• •Optimize key business metrics such as CPC, CPA, and ROAS
• •Design robust data workflows for front-end tracking and feature engineering
• •Solve complex multi-objective problems for ranking and bidding
• •Influence product decisions through experimental design and A/B testing
• •Build and deploy large-scale AI systems with measurable business outcomes
• •Define and drive technical strategy for Data Science and ML initiatives
• •Mentor engineers and scientists to align on high-impact goals
• •Contribute to auction design and dynamic pricing models
• •Apply reinforcement learning and causal inference to business challenges
• •Design experimentation methodologies for feature rollouts
• •Familiarize with recommendation systems and demand forecasting models

Tools & Technologies

Benefits