Senior Data Scientist (AdTech)

We’re looking for a curious and driven Senior Data Scientist who is ready to take on real product challenges.

If you love building intelligent systems, improving performance, and working without micromanagement, you’re in the right place at the right time!

Customer

Our client is a fast-growing player in the AdTech space, focused on building advanced technology solutions that help digital publishers optimize revenue and better engage with their audiences. Their platform uses cutting-edge machine learning to drive smarter decision-making across the advertising lifecycle.

Requirements

-Degree in computer science, machine learning, statistics, mathematics, or a related field

-5+ years of experience applying ML and data science in production environments, ideally in the AdTech or digital media sector

-Proven experience of deploying machine learning models in live systems

-Strong proficiency in Python, SQL, familiarity with TensorFlow, PyTorch, and cloud-based ML services (e.g., AWS SageMaker, Snowflake, etc.)

-Hands-on experience with real-time data processing frameworks such as Spark, Flink, or Kafka

-Deep understanding of probabilistic modeling, optimization techniques, and experimentation frameworks (A/B testing, multi-armed bandits)

-Knowledge of the AdTech ecosystem, including RTB, DSP, SSP, and auction theory

-Proven ability to analyze and interpret large, complex datasets with precision and insight

-Upper-Intermediate level of English

Personal Profile

-Strong communication skills with the ability to translate technical concepts to diverse audiences

-Deep interest in innovation and a strong sense of responsibility for the quality and impact of the work delivered

Responsibilities

-Develop and deploy scalable machine learning models to improve advertising performance and revenue outcomes

-Design and enhance recommendation systems and predictive algorithms tailored to publisher monetization

-Build decision systems that operate in real-time and at scale

-Collaborate with data engineering teams to create robust and efficient data pipelines

-Apply advanced ML techniques such as reinforcement learning, deep learning, and causal inference

-Integrate ML-driven features into production systems and communicate outcomes effectively to technical and non-technical stakeholders

-Stay informed on emerging developments in programmatic advertising, predictive modeling for user engagement, and data science approaches that prioritize user privacy