Machine Learning Scientist 5 – Ads Forecasting

🕒 July 7

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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 that predict campaign delivery outcomes — delivery risk, reach, frequency, and contention — to replace the current simulation engine. • Model demand-side campaign outcomes while incorporating supply-side signals, so the models reason about how well available inventory matches what advertisers are trying to achieve (targeting, frequency caps, contention, pacing). • Design rigorous offline and online evaluation frameworks to measure model accuracy, robustness to seasonality and distribution shift, and lift over the simulation baseline. • Own feature engineering and contribute to the team's feature store — turning ad-serving logs, campaign attributes, and supply signals into reusable, well-documented features. • Prioritize explainability and interpretability: your models' outputs must be defensible to sales and media-planning stakeholders making real booking and underwriting decisions. • Partner with ML engineers to deploy models at scale and to monitor production model health and drift, feeding monitoring insights back into the next modeling iteration. • Collaborate with cross-functional partners across product, engineering, and sales to define objectives, constraints, and trade-offs, and to drive adoption of ML-driven forecasts. • Communicate technical decisions, trade-offs, and results clearly to both technical and non-technical audiences at all levels of the company.

🎯 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 (e.g. gradient-boosted trees, regression, and related methods) with a strong bias toward interpretable, explainable models. • Strong feature engineering skills and familiarity with feature stores and standard ML lifecycle practice (versioning, evaluation, monitoring, 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 — how campaigns are delivered and what creates delivery risk: targeting, frequency caps, contention, bidding, pacing, budget planning, and the core campaign objects/attributes; and the metrics that matter (reach, frequency, impressions, clicks, outcomes). • You should understand both the supply side (ad-serving rules and inventory behavior) and the demand side (campaign attributes and advertiser goals). • Ads experience is strongly preferred. • Ability to work independently, drive your own projects, and make compelling cases for prioritization. • Ability to communicate technical and statistical concepts clearly to audiences at many levels. • Embodies the Netflix values while bringing a new perspective to continue improving our culture.

🏖️ Benefits

• 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 • Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. • Full-time salaried employees are immediately entitled to flexible time off.