We need your help.
Decision Point is changing the fundamentals of patient and provider engagement. For years, health plans have relied on descriptive data and reactive engagement. We empower our clients to understand and predict the whole member journey, enabling sustained improvements in member health outcomes and plan performance. We combine the latest, most practical technologies and a deep understanding of healthcare, bringing innovative, pragmatic solutions to an industry that touches us all.
We are looking for an experienced engineer to help us design and implement ML and AI systems that will help us meet our growth and scale goals and continue to provide high quality and impactful software to our customers.
At Decision Point, the ML Platform team is aimed at building an industry leading AI/ML platform that can scale with our customer base.
- Work alongside our Data Scientists and Product Engineers to collaborate on various ideas and ensure that there is a highly reliable, world class platform to run their ML models on
- You will help build high performance and flexible infrastructure and services that can rapidly evolve to handle new technologies, techniques, and modeling approaches
- You will implement and operate an intuitive, easy to use and flexible ML development framework
- Work with us on advancing our AI capabilities including optimization and recommendation engines
Skills and experience
- B.S., M.S., or PhD. in Computer Science or equivalent
- Exceptionally strong knowledge of CS fundamental concepts and OOP languages
- 4+ years of industry experience
- Prior experience building machine learning systems in production such as enabling data analytics at scale
- Prior experience in machine learning - you’ve developed and deployed your own models - even if there are simple proof of concepts
- Systems Engineering - you’ve built meaningful pieces of infrastructure in cloud computing environment
- Proficient with SQL, Python and Pandas / Python machine learning libraries
- Strong understanding of relational data, ETL practices, and OLAP databases
- Familiar with Spark, MLLib, Databricks MLFlow, Apache Airflow or similar technologies
- Familiar with a cloud based environment such as AWS
- Familiar with Docker