Machine Learning Scientist 5 – Forecasting Aggregation

Netflix

Machine Learning Scientist building Netflix’s ad-campaign forecasting models. Developing interpretable predictions for inventory, delivery risk, reach, and frequency at scale.

Posted 8/4/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $466,000 - $750,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying supervised machine learning models, with a strong focus on feature engineering, model evaluation, and collaboration across teams. Proficient in communicating complex technical concepts to diverse audiences while ensuring model explainability and interpretability.

Highest-signal resume keywords

Supervised LearningFeature EngineeringPython ProgrammingSQL ProficiencyMachine Learning Model Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ModelsGradient-Boosted TreesRegression AnalysisModel EvaluationFeature StoresAlgorithm PrototypingData ValidationAd-Serving ConceptsCampaign AttributesStatistical Analysis

Soft Skills

Independent WorkProject ManagementClear Communication

Tools & Technologies

DSP PlatformsSSP PlatformsMetaflowLarge-Scale ML Tooling

Certifications & Qualifications

PhD in StatisticsMaster's in MathematicsMaster's in Computer Science

Industry Keywords

Campaign Delivery OutcomesTargetingFrequency CapsContentionBiddingBudget PlanningReachImpressionsClicksAd Experience

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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