Your Mission:
- Collaborate with data scientists, analysts, business and IT teams to design, implement, and deploy Machine Learning and AI services using Python, PySpark, Azure services, Databricks, Terraform, and other open-source software.
- Lead by example, to develop plans, designs, code, and documentation, and to perform code reviews, testing, and debugging.
- Direct and maintain best practices within the data science community with automated, repeatable data pipelines.
- Create templates for data scientists, embracing leading-edge modeling techniques
- Establish a Feature Store with tens of thousands of features available to use in predictive models
- Define monitoring, auditing, data lineage, and dependency tracking for data pipelines to improve quality
- Integrate ethical AI bias monitoring and drift analysis into modelling processing
- Standardize the definitions of clinical, financial, and actuarial features for use across the business
- Build and deploy ML models on Azure, using best practices in security, and Azure services
- Understand best practices for effective predictive models, including cluster management and algorithms, and evangelize with Humana Data Scientists
- Thrive in a fast-paced agile environment, closely interacting with business analysts, software engineers, machine learning scientists, data engineers, and data scientists
- Mentor junior team members, to help grow and mature the overall skillset of the team
Required Skills/Experience:
- 5+ years exp as ML Engineer with significant Python experience
- Experience with Cloud platforms
- Experience deploying models
- Experience with Pandas/Pyspark in work environment or via self-study
- Solid knowledge of data structures and algorithms
- Ability to learn new technologies and comprehend unfamiliar data domains and business processes quickly
- BS in Computer Science or related field (potential academic foundations of interest-Mechanical/Electrical engineer, Physics, Math, Statistics as a minor.
Areas of Interest to share (not required):
- Open-source contributions and portfolio of shipped code at GitHub
- Familiarity with Healthcare industry
- Sample projects of innovation with ML/AI/NLP, i. e.; Kaggle competitions and Hackathons