Machine Learning Scientist 5 – Forecasting Aggregation
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Job Description
Machine Learning Scientist building Netflix’s ad-campaign forecasting models. Developing interpretable predictions for inventory, delivery risk, reach, and frequency at scale.
Responsibilities:
- Build, prototype, and iterate on supervised machine learning models predicting campaign delivery outcomes, including delivery risk, reach, frequency, and contention
- Model demand-side campaign outcomes using supply-side signals such as targeting, frequency caps, contention, and pacing
- Design offline and online evaluation frameworks for accuracy, robustness, distribution shift, and improvement over the simulation baseline
- Own feature engineering and contribute to the team feature store using ad-serving logs, campaign attributes, and supply signals
- Ensure model explainability and interpretability for sales and media-planning stakeholders
- Partner with ML engineers to deploy models at scale and monitor model health and drift
- Collaborate with product, engineering, and sales to define objectives, constraints, and trade-offs and drive adoption of ML-driven forecasts
- Communicate technical decisions, trade-offs, and results to technical and non-technical audiences
Requirements:
- Advanced degree (PhD or Master's) in Statistics, Mathematics, Computer Science, or a related quantitative field
- 5+ years of relevant experience building machine learning models on large-scale data
- Deep expertise in supervised learning, including gradient-boosted trees and regression
- Strong feature engineering skills
- Familiarity with feature stores and standard ML lifecycle practices, including versioning, evaluation, monitoring, and retraining
- Proven ability to prototype algorithms and validate them rigorously against production data
- Strong programming skills in Python and strong SQL
- Working knowledge of ad-serving and campaign concepts, including targeting, frequency caps, contention, bidding, pacing, budget planning, and campaign objects and attributes
- Understanding of reach, frequency, impressions, clicks, and outcomes
- Understanding of both supply-side ad-serving rules and inventory behavior and demand-side campaign attributes and advertiser goals
- Ads experience strongly preferred
- Ability to work independently and drive projects
- Ability to communicate technical and statistical concepts clearly to audiences at many levels
- Experience with DSP, SSP, or publisher-side ad platforms is nice to have
- Familiarity with Metaflow or comparable large-scale ML tooling is nice to have
- Experience partnering with ML engineers to ship and monitor production ML systems is nice to have
- Experience creating data products, dashboards, or explainability tooling is nice to have
- Experience applying GenAI to developer or research productivity is nice to have
Benefits:
- Annual salary-only compensation structure with choice between salary and stock options
- Health Plans
- Mental Health support
- 401(k) Retirement Plan with employer match
- Stock Option Program
- Disability Programs
- Health Savings and Flexible Spending Accounts
- Family-forming benefits
- Life and Serious Injury Benefits
- Paid leave of absence programs
- Flexible time off for full-time salaried employees
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