Masters/Ph.D. in a quantitative field such as Operations Research, Mathematics, Statistics, Industrial Engineering, Mechanical Engineering, Physics, or related field
5+ years of experience in a data science or applied machine learning role, ideally in a high-scale, real-time environment
Proficient with applied machine learning techniques (classification, regression, evaluation metrics, etc.), especially tree-based models like XGBoost
Comfortable navigating large, complex legacy codebases and proposing changes to production systems
Strong programming skills in Python and SQL
Experience with Scala/Apache Spark for distributed data processing and machine learning at scale
Familiar with Deep Learning using frameworks such as PyTorch/Tensorflow
Strong written and verbal communication skills — able to synthesize technical findings into clear, actionable insights
Self-directed, curious, and able to thrive in a collaborative environment
Responsibilities
Design, implement, evaluate and help deploy machine learning models for bidding, pacing and KPI prediction (Click, Action, Viewability etc.)
Analyze large-scale behavioral and performance data using Spark (Scala/PySpark), SQL, and Python
Deep dive into complex, legacy codebases; comfortable navigating and improving existing systems
Collaborate with product managers, engineers, and other data scientists to define problems and deliver end-to-end solutions
Write clear, concise technical documentation and communicate insights effectively across both technical and non-technical stakeholders