Rebecca L. Paris's Linkedin Analytics

FROM DATA & INFORMATION TO INSIGHTS-DRIVEN STRATEGY Operations Research ● Data Analytics / Science ● Risk Management ● Governance ● Decision Modeling HIGHLIGHTS: *TS/SCI security clearance, active; awarded April 2024 *20 years’ experience designing / improving frameworks, practices & guiding new strategies *Apply systematic operations research, & analytical methods to drive business & operations excellence *Sectors: financial, higher education, scientific research, & military (combat sys, intelligence, & aviation) *Value added – asset management, risk-compliance management, customer success, & preserve funding *Autonomous competitive learning habits for exploiting opportunities & staying ahead of new threats BACKSTORY: Since middle school math and science, I’ve lost track of time working to understand and solve problems. Upon graduation from a math/science magnet high school, I joined the Navy. During my 6yr service, I was on many teams and saw new places – foreign and domestic: - OCONUS: UAE, Bahrain, Australia - CONUS: FL, DC, NY, HI, CA PRESENT DAY (Continuous development): * Navigating & competing in project-based workforce environments - Soft skills: creating engaging / value-add content - Tech skills: Python integrations - AI, API, Optimization, MS Excel CORE BUSINESS COMPETENCIES * Reverse-engineer what success looks like * Key drivers, assumptions, & constraints inform decision-making & guide project direction * Transform messy data into insights * Hypotheses—prove, disprove, revise * Cross-functional collaboration & engagement * Lend objectivity & facilitate common fact-basis * Risk-Informed solutions * Diagnose ▲ Deconstruct ▲ reDesign * Governance - ensure most current standards are upheld * End-user experience at forefront of design STRATEGY, ANALYTICS * Customer: segmentation, retention, lifecycle; satisfaction, engagement * Statistics ** Inferential: probability theory, Bayesian ** Predictive: time series, survival analysis ** Analysis of variance, error * Machine learning ** Regression: linear, logistic, ridge, lasso ** Decision trees, cluster, neural network * Monte Carlo simulation * Athey multi-criterion decision * One-Hot Encoding Variable Selection TECHNOLOGY * Python – numpy, pandas, seaborn, Scikit-Learn, Plotly-Dash, AI, API * GitHub * SQL – PostgreSQL, Microsoft SQL Server * SAS * MS Excel, Access, PowerPoint, Word, Teams * Trello, Slack * Salesforce * SharePoint