You will have the opportunity to;
Assemble large, complex data sets that meet functional / non-functional business requirements.
Compare and analyze various methods through Proof of Concept to find the optimal solution and apply it in production.
Design and build data transformation and data structures.
Work in a cross-functional team of Machine Learning engineers and Data scientists to design and code large-scale batch and real-time data pipelines.
Develop performant data models utilizing BI and AI/ML systems.
Construct data models for Machine Learning infrastructure.
Your background will include;
Degree in Computer Science or a related form of Science or Engineering training.
5+ years of relevant work experience.
Security Plus certification or the willingness to obtain certification quickly.
Advanced knowledge in model evaluation, tuning and performance, operationalization and scalability of scientific techniques, and establishing decision strategies.
Experience with automated, process-driven data-flows using ML algorithms and resources.Experience with NLP across text, imaging.
Experience with Amazon Web Services, Azure, and/or Google Cloud Platform. Strong, expert-level knowledge in advanced SQL.
Deep understanding of data warehouse structures (OLTP vs. OLAP, Fact/Dimension, schemas, etc.).
Hands-on experience working with relational databases and ETL processes. Experience using BI tools to analyze large datasets.
Experience with big data tools.
Experience with relational SQL and NoSQL databases.
Knowledgeable of object-oriented/object function scripting languages: Python, Java, Scala, etc.
Knowledgeable of machine learning toolkits like spark mllib, H20, scikit-learn, R, and ML techniques.
Excellent problem-solving and analytic skills associated with working on unstructured datasets.
Experience with industry-standard medical data models, e.g. HL7, OMOP, ICD 9/10, a plus.
Experience with De-Identification and/or tokenization to meet HIPAA requirements is a plus.
Must be able to obtain a Public Trust clearance.
