Data Scientist (L4) – Messaging Data Science & Engineering – Netflix

Opportunity Overview

Experimentation & Causal Measurement Lifecycle

The Messaging Data Scientist drives decision-making for member communications across off-platform (email, push) and in-product (interstitials) channels, optimising targeting, timing, and content:

┌───────────────────────────────────────────┐
│ 1. Data Pipeline & Dataset Enrichment    │ ➔ SQL/Python pipeline engineering, data cleaning, automated telemetry
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Hypothesis Design & A/B Testing        │ ➔ Channel/timing/content experimentation, power calculations, sample splitting
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Causal Inference & Non-Experimental Ops│ ➔ Quasi-experiments, Synthetic Controls, Difference-in-Differences (DiD), IV
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Automation & Self-Serve Tooling        │ ➔ Automated test-read dashboards, campaign health checks, self-serve UI
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 5. Strategic Partnership & Scale          │ ➔ CMP cross-functional alignment, scaling to Live Event & Gaming verticals
└───────────────────────────────────────────┘

Key Responsibilities

• Rigorous A/B Testing: Design, execute, and analyze large-scale experiments evaluating messaging strategies across targeting, delivery timing, content variants, and channel selection.

• Causal Inference Modeling: Apply advanced statistical techniques (e.g., Synthetic Controls, Propensity Score Matching, Difference-in-Differences) when standard A/B tests are infeasible or contaminated.

• End-to-End Data Engineering: Author and maintain production SQL/Python data pipelines, ensuring reliability, reproducibility, and dataset enrichment for messaging metrics.

• Automation & Self-Serve Dashboards: Build automated experiment reads, campaign health checks, and self-serve diagnostic tools for cross-functional partners.

• Cross-Functional Partnership: Partner with the Consumer Messaging Program (CMP) and Product/Algo teams to turn ambiguous business asks into experimental frameworks.

Technical & Functional Qualifications

Baseline Qualifications

• Experience: 3+ years of hands-on data science experience focusing on experimentation and causal modeling.

• Technical Stack: Advanced Python and SQL skills, including data pipeline development, dashboard building, and task automation.

• Core Competencies: Expertise in statistical inference, experimental design, and translating complex data outputs into actionable recommendations for business leaders.

Preferred Technical Skill Matrix

Application & Interview Preparation

1. Causal Inference & Experimentation Depth: Prepare to defend experimental methodologies—specifically how to measure treatment effects when network effects, cannibalization, or user contamination prevent clean A/B splits.

2. Netflix Culture & Freedom/Responsibility: Review Netflix’s culture memo. Expect situational questions assessing self-direction, prioritization, and managing direct stakeholder relationships without guidance.

3. End-to-End Coding & Pipeline Scenarios: Practice live coding in Python and SQL focused on data aggregation, automated test reads, and metric calculation for high-volume event streams.