Nexxen Senior Data Scientist role focused on machine learning and large-scale data processing for Nexxen's Demand-Side Platform. Responsible for designing, implementing, and deploying ML models for bidding and forecasting.
Responsibilities
We’re looking for a self-directed, analytical, and curious Sr Data Scientist with 5+ years of hands-on experience to join our team working on the core optimization and bidding logic of the Nexxen Demand-Side Platform (DSP). You will work on problems at the intersection of machine learning, large-scale data processing, and ad delivery performance and your work will directly impact how we bid, win, and optimize impressions across real-time programmatic advertising exchanges.
This role will be based in the New York City office. Our team follows a hybrid schedule working in-office three days a week and remotely for the rest.
Each day can be different here at Nexxen, but some of the things you can expect to be doing daily are:
Design, implement, evaluate and help deploy machine learning models for bidding, pacing, KPI prediction (Click, Action, Viewability etc.) and Forecasting.
Analyze large-scale behavioral and performance data using Spark (Scala/PySpark), SQL, and Python.
Deep dive into complex, legacy codebases; you should be 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.
What Will I Bring?
Master’s in a quantitative field such as Data Science, Operations Research, Computer Science, 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.
Qualification
Comfortable using LLM-based tools (e
Required
Master’s in a quantitative field such as Data Science, Operations Research, Computer Science, Mathematics, Statistics, Industrial Engineering, Mechanical Engineering, Physics, or related field
5+ years of experience in data science or applied machine learning
Proficient with applied machine learning techniques, especially tree-based models like XGBoost
Strong programming skills in Python and SQL
Experience with Scala/Apache Spark for distributed data processing and machine learning
Preferred
Comfortable using LLM-based tools (e.g., Copilot, ChatGPT) to accelerate development, debugging, and documentation
