Aaron Slowey

Background

About Aaron Slowey

After a decade of graduate and post-graduate research in geochemistry, Aaron took an MBA and began a new career as an internal strategy consultant focused on advanced analytics and machine learning, advancing through a succession of roles to become a Director of Data Science at Bank of America in the Global Payments business. He has spent over 8 years contributing to large strategic change programs by communicating with stakeholders to understand which processes and technical methods are congruent with the culture, strategy, and operations of complex businesses.Aaron applies strategy, product management, data engineering, data science and machine learning, and software engineering to tackle complex problems and identify and clearly communicate the resources needed to launch program-level strategic initiatives and deliver value at enterprise scale from data. For example, he led the team that built a customer servicing application that nearly halves labor cost while maintaining repayment rates by feeding call centers instructions on how best to contact delinquent card holders. At BofA, Aaron coordinated with a third-party vendor to redevelop a large-scale forecasting application. His thorough understanding of the problem, judgment to identify high leverage improvements under resource constraints, and tact to lead without direct control led to a 30 percentage point increase in the likelihood to produce high-quality forecasts to over 1,500 of the bank's clients' accounts.Skills • Data analytics, exploration on large data sets using data mining algorithms and pattern recognition, statistical analysis, machine learning, predictive modeling, statistical modeling, behavioral modeling, optimization, scenario and outcomes analysis, clustering, regression • Data-oriented programming languages and libraries: Python, pandas, NumPy, scikit-learn, SQL, hyperopt, patsy, PyArrow, SciPy, statsmodels, statsforecast, logging, multiprocessing • Software testing and quality assurance: doctest, pytest, hypothesis, Git • Relational data warehouse technologies: Oracle, Teradata • Cloud technologies: OpenShift, Amazon Web Services (AWS) S3, Snowflake • Managing consulting engagements: requirements gathering, establish priorities, project plans, communicating progress reports, documenting analytics, findings, issue resolution, Jira, Confluence, Bitbucket • Data visualization and reporting: altair, matplotlib, Tableau • Model risk management