Machine Learning Scientist 5- Forecasting Aggregation

Netflix
Panama
Workplace: RemoteFull timeUSD 466,000 - 750,000 annuallyFunction: Data Science & Machine LearningExperience: 5+ yearsEducation: mastersSkills: ["Communication","Independent work","Project prioritization","Explainability","Rigorous validation"]

Build and prototype supervised machine learning models that predict Netflix ads campaign outcomes, replacing a simulation-based forecasting engine. Own the modeling workflow end-to-end, including feature engineering, rigorous offline/online evaluation, and interpretability for decision-makers. Collaborate with ML engineering to deploy models at scale, monitor production health and drift, and partner cross-functionally across product, engineering, and sales to drive adoption of ML-driven forecasts.

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FursaFursa
Netflix
Netflix
1 day ago

Machine Learning Scientist 5- Forecasting Aggregation

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Last checked: 4 hours agoStatus: Live

Job Summary

Build and prototype supervised machine learning models that predict Netflix ads campaign outcomes, replacing a simulation-based forecasting engine. Own the modeling workflow end-to-end, including feature engineering, rigorous offline/online evaluation, and interpretability for decision-makers. Collaborate with ML engineering to deploy models at scale, monitor production health and drift, and partner cross-functionally across product, engineering, and sales to drive adoption of ML-driven forecasts.
Location: Panama
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Build, prototype, and iterate on supervised ML models to predict campaign delivery outcomes (delivery risk, reach, frequency, contention).
  • •Model demand- and supply-side signals to match inventory availability with advertiser objectives such as targeting, frequency caps, contention, pacing, and budgeting.
  • •Create rigorous offline and online evaluation frameworks to measure accuracy, robustness to seasonality and distribution shift, and lift vs the simulation baseline.
  • •Own feature engineering and contribute to the team’s feature store using ad-serving logs, campaign attributes, and supply signals.
  • •Partner with ML engineers to deploy models at scale, monitor drift/health in production, and communicate trade-offs and results to technical and non-technical stakeholders.

Pay and Benefits

Salary: USD 466,000 - 750,000 annually
Equity and Bonus:Equity
Perks:Health Insurance401kEquityDisability InsurancePaid LeaveRetirementFlexible SpendingLife Benefits

Key Requirements

  • •Advanced degree (PhD or Master's) in Statistics, Mathematics, Computer Science, or a related quantitative field.
  • •5+ years building supervised machine learning models on large-scale data.
  • •Deep expertise in supervised learning with a strong bias toward interpretable, explainable models.
  • •Strong feature engineering experience with feature stores and the ML lifecycle (versioning, evaluation, monitoring, retraining).
  • •Strong programming skills in Python and SQL, plus working knowledge of ad-serving and campaign concepts (delivery risk, metrics, and core campaign attributes).
Experience:5+ yearsAd-techLarge-scale dataSupervised learning
Education:Master's
Skills:CommunicationIndependent workProject prioritizationExplainabilityRigorous validation
Tech Stack:PythonSQLGradient-boosted treesMetaflow

Company Brief

Netflix
Global subscription streaming service that produces and distributes movies, TV series, and games across multiple devices, operating worldwide with extensive original content and localized offerings.
Industry: Streaming Platforms
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Los Gatos, United States
Founded: 1997
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Glassdoor: 4.1
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