Note: The job is a remote job and is open to candidates in USA. Netflix is a leading entertainment company focused on pushing the boundaries of storytelling and technology. They are seeking a Machine Learning Scientist to build predictive models for their new ad business, focusing on campaign delivery and inventory forecasting.
Responsibilities
- 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
Skills
- 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
- Experience at a DSP, SSP, or publisher-side ad platform where predicting campaign outcomes at scale is a core science problem
- Familiarity with our ML stack (Metaflow) or comparable large-scale ML tooling
- Experience partnering with ML engineers to ship and monitor production ML systems
- Experience creating data products, dashboards, or explainability tooling for non-technical stakeholders
- Experience applying GenAI to boost developer/research productivity
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
Company Overview
Company H1B Sponsorship