Senior Data Scientist, ML - Fraud Detection & Effectiveness

Adobe

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
San Jose, CAAustin, TXSeattle, WANew York, NYChicago, IL
Salary
$163,200–$236,400 / yr
Posted
5 days ago
Freshness
Confirmed live today

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $205k
This role $200k
$153k most similar roles pay here $263k

This role pays less than 55% of similar roles. Most pay $164,643–$246,150 — the shaded band above. At the midpoint, this role pays about $200k versus about $205k for comparable roles.

Based on 240 similar postings.

Employer

About Adobe

Adobe Inc. is a global software company known for creative and multimedia software products including Photoshop, Illustrator, Acrobat, and its cloud-based Creative Cloud and Document Cloud suites. Industry: Creative & Digital Experience Software

Adobe currently has 211 open roles on FindRole.

Listed pay typically runs $183,300–$265,350 across 209 roles with salary data.

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At a glance

TL;DR · Senior Data Scientist, ML - Fraud Detection & Effectiveness

Senior Data Scientist, ML— Fraud Detection & Effectiveness joins the team to build fraud and abuse detection models while measuring their effectiveness through experimentation and analytics. This role involves developing robust evaluation frameworks, defining ground truth and labeling approaches, and analyzing model drift and emerging fraud patterns. The candidate will design experiments to evaluate trade-offs between precision, recall, and customer impact while building dashboards to translate performance into business insights. Key technical requirements include proficiency in Python, SQL, and large dataset management, alongside expertise in statistical methods, classical machine learning, and anomaly detection. The role addresses the critical problem of identifying fraudulent activity within adversarial domains. Preferred skills include experience with weak supervision, active learning, and LLMs for evaluation to improve automated decisioning systems and multi-layered risk controls across the fraud lifecycle.

What you'll do

  • Build and tune machine learning models for fraud and abuse detection using statistical and classical techniques.
  • Develop robust evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness.
  • Analyze false positives, negatives, model drift, and emerging fraud patterns to improve detection systems.
  • Define ground truth, labeling approaches, and fraud taxonomies to support reliable model development.
  • Design experiments to evaluate trade-offs between precision, recall, customer impact, and fraud loss.
  • Create dashboards that translate complex detection performance into measurable business impact for stakeholders.
  • Pressure-test models and data for leakage, bias, and other sources of misleading results.

What we're looking for

  • 8+ years of experience in applied Data Science/ML building and evaluating production models.
  • Strong foundation in statistical and classical ML, experimentation, model evaluation, and performance measurement.
  • Proficiency in Python and SQL for working with large, complex datasets.
  • Experience with model monitoring, drift analysis, and handling imperfect or delayed labels.
  • Ability to translate complex data into clear insights through visualization and storytelling.
  • Bachelor's degree in Statistics, Mathematics, Computer Science, or a related field (advanced degree preferred).
  • Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains (preferred).
  • Experience with anomaly detection, clustering, behavioral modeling, or LLMs (preferred).

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