Sarah Nibs Email & Phone Number

San Jose, California, Us

I build the data-science side of systems that decide whether it's a good idea to accept payment and ship out a product that someone ordered online, or if the transaction is too likely to be fraudulent. This means machine learning models: feature engineering & implementation, model training & evaluation, implementing business tradeoffs, monitoring & reacting to fraud trends, etc.But also it means just kinda vibing with what areas it feels like Signifyd is currently papering over rather than understanding deeply, and gradually figuring out how to understand them. And that's the most fun part.

San Jose, California, Us

just noticed my role changed a while ago, now I'm in the data science org, doing data science-y things, and also software engineering-y things, y'know how it goes

San Jose, California, Us

"Tech lead"ing team of half a dozen software engineers building the infrastructure to make fraud/not-fraud decisions using machine learning models- facilitate agile process - plan sprints, hold standups, grooming, retrospectives, etc. PM and people-manager support.- multiply team - mentor, unblock, scope/design projects, sync cross-team, etc.- write code - integrate information from e.g. machine learning models and turn it into real utility. Stack currently involves: Java, Python, AWS[various], Kubernetes, Airflow, Cassandra, MySQL, Redshift, H2O- hiring - technical deep dive interviews for all engineering positions

San Jose, California, Us

Managing team of half a dozen software engineers building the infrastructure to make fraud/not-fraud decisions using machine learning models- implement agile process - plan sprints, hold standups, grooming, retrospectives, etc.- grow team - mentor, unblock, help pick good projects, organize learning sessions, etc.- write code - third party data integration, decisioning microservices, internal feature data generation, etc. (Stacks currently involve: Java, Python, AWS[various], Kubernetes, Airflow, Cassandra, MySQL, Redshift, Hadoop/Hive, Weka, H2O)- hiring - technical deep dive interviews for all engineering positions(as the company grew, our team's responsibilities changed somewhat, handing off model experimentation and building to our new datascience team - we're much more infrastructure-y now!)

San Jose, California, Us

Managing team of half a dozen software engineers building the machine learning models powering fraud decisions- implement agile process - plan sprints, hold standups, grooming, retrospectives, etc.- write code - feature engineering, model development, etc. (Java, Python)- technical deep dive interviews for all engineering positions

San Jose, California, Us

- invent domain-specific random forest evaluation for extremely noisy target class (Python, Java)- improvements to third party integration infrastructure (Java)- training and deploying new ML models (Python, Java)

Seattle, Wa, Us

- X-Ray for Books natural language processing and other machine learning (Java)- rapid iteration on Notable Clips algorithm, launching with over 150,000 titles (Java, Hadoop)- scaled design of operator web app to handle large books and anthologies (Javascript)

Redmond, Washington, Us

- scaled context-sensitive spellchecking to support quick development for new languages (C++, C#)- designed and built tokenization and entity recognition for 30+ languages (C++)- prototyped machine learning writing assistance features (C#)

Bs, Mathematics

Bs, Mathematics