Machine Learning Scientist 5 – Forecasting Aggregation

🕒 August 4

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Netflix

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1997

📱 Media

👥 B2C

Media • B2C

Netflix is a global streaming entertainment company and content producer whose stated mission is "to entertain the world. " It operates a consumer-facing subscription platform offering on-demand TV shows, films, and original programming, and also runs a public careers site emphasizing culture, inclusion, and hiring accommodations. The provided text highlights Netflix’s focus on recruiting talent worldwide, its work-life and culture pages, and its public-facing employer materials.

📋 Description

• 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