Identify root cause, resolve, test, and document fixes for defects and minor enhancements by following through appropriate change management processes.
Ensure that Machine Learning (ML) applications/algorithms Pipelines are working in production environment Proactively capture and ensure alignment to daily operational performance metrics, ensuring end-to-end visibility on the health of the Machine Learning (ML) applications/algorithms.
Deploying, monitoring and troubleshooting of Machine Learning (ML) applications/algorithms.
Ensure that internal production support tools and documentation are accurate and continually up to date.
Proactively work with internal delivery teams to get to the root cause of data processing or other systems issues.
Ensure that deviations from standard processes and routines are identified, captured and work through to resolution as defined by standard tools and processes.
Ensure that Channel (Consumers) Integration capabilities and partnerships are well understood, documented Help govern and support Customer framework and utilization.
Proactive in recognizing potential operation issues and risks, actively bringing timely insights and suggestions to bear in mitigation strategies.
Analyzes and interprets collected operations data, developing reports and analytics techniques to spot underlying trends and opportunities for improvement.
Work directly with internal and external teams to understand the holistic end-to-end role of Machine Learning algorithms and models.
Ensure the security and integrity of solutions including compliance with Navy Federal, industry engineering and Information Security principles and practices.
Recognize potential issues and risks during the analytics project implementation and recommend mitigation strategies.
Document best practices for data analysis, data engineering, and evangelize their usages.
Develops reports and presentations for senior management, as needed.
Key point of contact between the data analyst/data engineers and the Production support teams
Perform other duties as assigned
Bachelor’s degree in Information Systems, Computer Science, Engineering, or related field.
Proficient in building and operationally supporting cloud enabled enterprise solutions leveraging Azure IaaS/PaaS; Azure Data Lake, Azure Machine learning, ML Studio, Data Factory, Apache Spark / Azure Databricks capabilities in financial services, or similar, sector.
Familiarity with Machine learning workflow, Text analytics, Regression, and Classification algorithms.
Experience with a Machine Learning (ML) technology stack such as R, Python, Azure ML, Azure HD Insights
Hands-on experience supporting Model training, evaluation, validation, serving and monitoring.
Proficient using enterprise logging and monitoring tools such as Azure Log Analytics and Splunk to develop complex queries to provide data driven operations support for Analytics & BI solutions.
Proficient with tools such as Service Now to track and manage production Incidents.
Able to understand data models, large datasets, business/technical requirements, BI tools, data warehousing, statistical programming languages and libraries.
Experience in working with enterprise change management tools by following ITIL best practices.
Experience in designing data lake storage structures, data acquisition, transformation, and distribution processing.
Advanced experience in SQL programming
Experience with agile delivery using Jira, or Azure DevOps.
Ability to understand and support highly complex data processes and analytical models which support strategic business outcomes.
Excellent written and oral communication skills.
Minimum three to five years of experience in supporting complex operations in large organizations
ITIL Certification in service operations
Ability to interpret complex mainframe copybooks, and relational database concepts
Prior experience in the Financial Industry
Follow instructions, standards, and procedures