Lead AWS Sagemaker
Onsite Cincinnati, OH
Duration: 12 Months +
AWS Sagemaker to expand Feature Store, introduce Model Registry, CI/CD, Real-Time models for our large data science credit models.
- The squad is currently working on an in-house build of Feature Store to help speed up modeling process for our Data Science department. Combination of Snowflake, Cloud Pak for Data. (More on this later)
- Currently, data scientist build model features (attributes) about customers in their own Jupyter notebook that feed into their models and never reuseable for others… aka reason for Feature Store
- They are also working on building real time scoring framework for our loan/card application process. Right now it’s batch and can be almost 31 days behind.
- Technology used: Docker, Kafka, Snowflake, Feature Store
- This is the most important part: They are working on bringing in AWS Sagemaker as a replacement for IBM Cloud Pak for Data. This is where we deploy our critical production models and where all most of modeling is done at the bank.
- We need someone that has been through standing up AWS Sagemaker into their company and/or someone that can deploy models in AWS Sagemaker.
- We are in early innings with Sagemaker and just scratching the surface. We need help getting this platform stood up