Principal Data Scientist

Principal Data Scientist

Requirements

• PhD or Master’s degree in Computer Science, Machine Learning, Statistics, or related fields; Or, equivalent experience
• 5+ years of experience in machine learning, data science, and predictive modeling, particularly in ad tech, audience segmentation, and bidding optimization
• Strong proficiency in Python, Scala, or R, along with ML libraries such as TensorFlow, PyTorch, or Scikit-learn
• Expertise in big data processing with Spark, Hadoop, or similar technologies
• Experience with real-time ML models and inference pipelines for ad targeting
• Deep understanding of auction dynamics, programmatic advertising, and bid landscape modeling
• Strong knowledge of privacy-preserving ML techniques such as federated learning and differential privacy
• Ability to translate business objectives into scalable data science solutions that drive measurable impact
• Excellent problem-solving skills and experience working in cross-functional teams with data engineers and product managers.

Responsibilities

• Work closely with data engineers, product managers, and sales teams to align data science initiatives with business goals.
• Design, train, and deploy machine learning models for audience segmentation and ad targeting.
• Analyze and interpret large-scale datasets to extract meaningful insights for monetization strategies.
• Develop and optimize real-time ML inference pipelines for ad decisioning.
• Experiment with different modeling techniques, A/B tests, and reinforcement learning strategies to improve performance.
• Collaborate with privacy and legal teams to ensure compliance with data regulations while maximizing model effectiveness.
• Collaborate with advertisers to understand precise targeting goals and campaign objectives.
• Analyze multi-dimensional customer data to identify meaningful segmentation variables.
• Design and develop sophisticated audience segmentation models using machine learning and statistical techniques.
• Engineer customer features that capture nuanced behavioral and demographic characteristics.
• Transform complex audience requirements into implementable segment definition logic.
• Validate segment performance through rigorous statistical testing and metric analysis.
• Continuously refine segmentation strategies to improve targeting precision.
• Present findings, insights, and model performance improvements to leadership and stakeholders.

Preferred Qualifications

• Experience working in large-scale consumer or ad tech platforms.
• Understanding of multi-touch attribution models and marketing measurement methodologies.
• Exposure to reinforcement learning and optimization techniques in ad bidding systems.
• Contributions to open-source machine learning projects or relevant research publications.