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AI Data Drift Early Warning System Developer

Aubrey Okafor

Full-time · Job · New_York

About the role

Model accuracy degrades silently when the data distribution shifts — a phenomenon called data drift. A fraud model trained on pre-pandemic transaction patterns misses post-pandemic buying behavior. A recommendation engine tuned for summer browsing patterns fails in December. By the time someone notices, the model has been underperforming for weeks. Our early warning system detects drift before it impacts model performance. You'll build the monitoring layer that compares incoming data distributions against training baselines using statistical tests calibrated for each feature type: KS tests for continuous features, chi-squared for categoricals, and embedding drift for high-dimensional inputs. The system must distinguish meaningful drift from normal variation without flooding teams with false alarms. The model monitoring team is four engineers. You'll own the drift detection module, working alongside the performance monitoring engineer who tracks model accuracy metrics. Your drift alerts should fire before their accuracy metrics drop — that's the whole point of early warning.

Responsibilities

  • Build data drift detection for continuous, categorical, and embedding features
  • Calibrate statistical tests to minimize false alarms while catching real drift
  • Design adaptive baselines that account for expected seasonal variation
  • Create drift severity scoring for alert prioritization
  • Integrate drift monitoring into existing MLOps deployment pipelines
  • Evaluate detection lead time: how far ahead of accuracy degradation does drift fire

Requirements

  • 5+ years in ML engineering with monitoring or data quality experience
  • Strong statistical background for distribution comparison tests
  • Experience with production ML systems and their failure modes
  • Proficiency in Python and SQL for metric engineering
  • Understanding of when statistical drift translates to business impact

Benefits

  • Fully remote with flexible schedule
  • Health, dental, and vision insurance
  • $3,000 annual learning budget
  • Stock options
  • Solve one of the hardest problems in production ML

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Categories

MLOps & AI Infrastructure data science & analytics

Skills

python pytorch sql docker aws

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Level

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Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure data science & analytics

Skills

python pytorch sql docker aws statistics

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