Machine Learning Operations Sr. Data Scientist @

Role: Machine Learning Operations Sr. Data Scientist

Company: Ascension

Location: Chicago, IL

Required Technical and Professional Expertise:

Develop advanced end-to-end ML models for different use cases: tabular data, images, video, speech, unstructured text.
Leads discussions with project stakeholders, senior management, and the user community, to collaboratively define project scope and capabilities in accordance with data science best practices and organizational needs. Clearly communicates technical knowledge and provides training in a client-facing technical consulting role.
Enhance, harmonize, and transform data from large relational databases to support ML projects (e.g. SQL, R, Python).
Create and manage a machine learning pipeline that ingests data, performs sophisticated feature engineering, and deploys a scalable and repeatable solution using serverless cloud ML tools. Architect, optimize, and deploy production ML systems on the cloud  (e.g., AWS, GCP).
Demonstrate leadership in a rapidly evolving environment. Proactively navigate ambiguity and technological hurdles to deliver state-of-the-art cloud data science solutions to healthcare problems.


High school diploma/GED with 2 years of experience, or Associate's degree, or Bachelor's degree required.

Work Experience:

1 year of experience required.
4 years of experience preferred.
2 years of leadership or management experience preferred.

Additonal Preferences:

Advanced degree in Computer Science, Mathematics, or other quantitative field; with some evidence of ML related work.
Proficiency in one or more SQL database technologies such as PostgreSQL, MySQL, Oracle, MS SQL, etc.
Experience experimenting, evaluating, tracking performance, and deploying production Machine Learning (ML) models with: TensorFlow, XGBoost, Spark ML, or Scikit Learn.
5 years of coding experience in one or more of the following languages: R or Python. Comfortable using Github for version control of code.
Experience working with production ML systems in the cloud (e.g. AWS, GCP). GCP preferred.