Sr. Machine Learning - Data Scientist

Job Summary
Our client is growing and the data processing landscape is shifting. We collect terabytes of data of various types, including real-time, from a cadre of sources from all of our clients. Our clients, large and small health systems, too are growing. The kinds and amount of data that each generates on a yearly basis is exponentially growing. Having this data improves our clients’ situational awareness, leading them to better understand how they are performing in order to improve their overall outcomes. This is true for the smallest community hospital to the largest health system.

Skills:
Some familiarity with deep learning.
3+ years of machine learning experience. Experience using predictive analytics in a business or healthcare setting.
Deep knowledge of a scripting or statistical programming language (Python preferred). Basic SQL. Ability to efficiently work with very large datasets. Ability to deal with non-standard machine learning datasets (class-imbalances, sparse matrices, etc.)
Comfortable writing complex SQL queries. Experience developing python packages. Version control experience in Git or a similar system. Experience with agile development practices. Experience with Hadoop. Experience with low-level machine learning libraries
3+ years of developing implementing machine learning algorithms using Python.
5+ years of SQL
2+ years working with open-source Big Data technology stacks (Apache Nifi, Spark, Kafka, HBase, Hadoop/HDFS, Hive, Drill, Pig, etc.) or commercial open source Big Data technology stacks (Hortonworks, Clouder, etc.)
3+ years with document databases (e.g. MongoDB, Accumulo, etc.)
2+ years of distributed version control system (e.g. git)
1+ years of experience in cloud-based development and delivery
Duties and Responsibilities
Use machine learning, data mining, and statistical techniques to create new, scalable solutions for business problems. 
Analyst and extract relevant information from business data to help automate and optimize key processes. 
Design, develop, and evaluate highly innovative models for predictive learning. 
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation. 
Research and implement novel machine learning and statistical approaches. 

Education:
Master’s Degree in Computer Science or other related field of study (preferred).
Bachelor's Degree in Computer Science or other related field of study (required).