mozilla-services/merino-py
Dec 2024 – Mar 2025
3 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentExperimentationAPI DevelopmentData ModelingHyperparameter TuningMachine Learning
Matt Cooper
PROFILE
Matt Cooper
Over a three-month period, contributed to mozilla-services/merino-py by delivering three backend features focused on experimentation, data integrity, and machine learning optimization. Developed and integrated a Thompson Sampling Prior Adjustment Experiment to refine recommendation relevance, employing Python for backend and API development. Standardized engagement data identifiers through backend refactoring, improving data modeling and simplifying downstream processes. Tuned GCS prior backend hyperparameters based on experimental results, increasing user engagement by adjusting prior weights and cleaning up legacy logic. All changes were thoroughly tested and integrated with existing protocols, demonstrating a methodical approach to experimentation, backend architecture, and data-driven feature delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
106
Activity Months3
Your Network
390 peopleSame Organization
@mozilla.com376
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Focused feature delivery and code hygiene in mozilla-services/merino-py. Key feature delivered: GCS prior backend hyperparameter tuning to boost user engagement by reducing the GCS prior weight from 0.1 to 0.05, based on an experiment showing increased total clicks. Cleaned up logic by removing an unused experiment enrollment check for modified priors and updated tests accordingly. All changes are tracked in a single commit tied to [HNT-349] with PR #806 (c1f7c60d60ba30f4e4703891386611508b4f71bb).
1 Commits • 1 Features
Mar 1, 2025March 2025: Focused feature delivery and code hygiene in mozilla-services/merino-py. Key feature delivered: GCS prior backend hyperparameter tuning to boost user engagement by reducing the GCS prior weight from 0.1 to 0.05, based on an experiment showing increased total clicks. Cleaned up logic by removing an unused experiment enrollment check for modified priors and updated tests accordingly. All changes are tracked in a single commit tied to [HNT-349] with PR #806 (c1f7c60d60ba30f4e4703891386611508b4f71bb).
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 Monthly Summary for mozilla-services/merino-py. Delivered the Engagement Data Identifier Standardization by refactoring the backend to use corpus_item_id, updating type hints, method signatures, and internal caching to ensure consistent data retrieval. This work improves data integrity and simplifies downstream usage. All changes were covered by tests and accompanied by an initial commit to pass tests (#801).
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 Monthly Summary for mozilla-services/merino-py. Delivered the Engagement Data Identifier Standardization by refactoring the backend to use corpus_item_id, updating type hints, method signatures, and internal caching to ensure consistent data retrieval. This work improves data integrity and simplifies downstream usage. All changes were covered by tests and accompanied by an initial commit to pass tests (#801).
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for mozilla-services/merino-py: Delivered a Thompson Sampling Prior Adjustment Experiment to reduce exploration, integrating new enrollment checks and aligning with the curated recommendations protocol and provider modules. Updated tests and ensured end-to-end coverage. This work enhances recommendation relevance while maintaining robust experimentation controls, delivering measurable business value through improved user experience and data-driven exploration.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for mozilla-services/merino-py: Delivered a Thompson Sampling Prior Adjustment Experiment to reduce exploration, integrating new enrollment checks and aligning with the curated recommendations protocol and provider modules. Updated tests and ensured end-to-end coverage. This work enhances recommendation relevance while maintaining robust experimentation controls, delivering measurable business value through improved user experience and data-driven exploration.
December 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance80.0%
AI Usage26.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentBackend DevelopmentData ModelingExperimentationHyperparameter TuningMachine Learning
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