⋄ Took intentional time to rejuvenate, reflect on my career goals and aspirations, and explore new career avenues in data science and entrepreneurship⋄ Gained proficiency in machine learning cloud architecture, Generative AI, advanced NLP modeling, and LLMOps through reading and coursework

⋄ Designed AWS/Azure cloud architectures and implementation blueprints to modernize client data platforms, focused on people, process, and technology⋄ Managed a matrixed team of six; responsible for overseeing revenue, setting team goals, and providing coaching, technical guidance, and mentorship⋄ Engaged in pre-sales activities including analyzing client needs and presenting tailored data and AI solutions, while also designing go-to-market ML & AI offerings to empower the sales team and effectively demonstrate the value proposition, leading to the successful acquisition of new business opportunities⋄ Executed seamless migration and optimization of client data pipelines to Databricks from alternative cloud and on-premise systems for enhanced performance, scalability, and model and data governance with cost reductions of up to 70%

⋄ Co-developed intellectual property to automate code conversion from Snowflake to Databricks using technologies such as Large Language Models (LLM), OpenAI, and LangChain, resulting in over 75% faster platform migrations and an invited presentation at the 2023 Databricks Data & AI Summit

⋄ Designed, optimized, and deployed a fine-tuned large language model to classify product descriptions into categories based on taxation regulations, achieving 95% accuracy with a +/- 3% margin of error across 90% of trained categories. Improved predictive performance through human-in-the-loop feedback and incorporated a custom confidence metric and rule-based hybrid solution for informed decision making⋄ Managed a team of three; provided mentorship, set performance expectations, and implemented an anonymous feedback system to address department concerns and improve morale, resulting in actionable insights reported to management during a period of high turnover⋄ Spearheaded a strategic cloud partnership with Databricks

Courses Taught:(3) ANA 600 - Fundamentals of Analytics(3) ANA 605 - Analytic Models & Data Systems

⋄ Collaborated with clients to identify business objectives and requirements, leveraging Azure cloud technologies to implement scalable, high-performance solutions with measurable results⋄ Led technical delivery and management of client expectations, project scope, budget, and timeline of projects involving the build and deployment of end-to-end data and AI pipelines for batch and streaming use cases⋄ Created project management and deliverable templates, blog posts, demonstrations, and sales content for clients and data/sales teams, leveraging MLOps, CPMAI, and Agile methodologies to enhance project execution, client engagement, and knowledge sharing⋄ Mentored junior data scientists and interns by providing technical training, advice, and ongoing support⋄ Advanced Leadership Program (2021-2022): Led a digital transformation initiative to enhance cross-sell capabilities and grow customer revenue. Implemented KPI tracking tools, a competency framework for cross-selling, organizational communication templates, and facilitated change management, resulting in improved data visibility, streamlined communication, and increased organization-wide revenue⋄ Notable Data Science/Engineering Projects:– Supervised ML/anomaly detection of up-coded E&M medical claims for faster detection and resolution of fraud and streamlined audit of claims– Supervised ML to predict delivery times and bottlenecks of inter-store transfers for a nationwide retail company– Streaming utility/meter data into a multi-hop architecture for custom analytics to detect and respond to outages and water main failures– Streaming machine data into event hubs for metric calculations and application insights for log data collection and dashboards– Custom fuel consumption and emissions calculations from OBD-II vehicle data and streamed into Databricks and CosmosDB storage

On-site at National University⋄ Used machine learning algorithms such as linear and logistic regression, XGB, random forest, SVMs, KNN, Naive Bayes, and clustering techniques to understand and predict student outcomes on an adaptive learning platform⋄ Led the design, preparation, and statistical analysis of research studies for presentations, grants, and publication for theoretical and applied applications⋄ Produced LaTeX/R reports and Tableau dashboards of formative assessment analysis results to ensure quality control and view student responses for faculty

Center for Research Methods and Data Analysis⋄ Provide consultations to students, faculty, staff, and external clients about research design, statistical analyses, and R/SAS code development⋄ Cleaned, joined, explored and modeled empirical research data in support of clients’ research projects⋄ Managed support, sales, technical delivery, and administration of REDCap, a HIPAA compliant survey administration and database tool for data collection⋄ Planned, facilitated, and taught workshops and published step-by-step guides about R, structural equation modeling, and other statistical methods

⋄ Led a research project analyzing strategies to model and visualize test speededness in several certification exams using SAS, R, and ggplot2⋄ Completed certification exam calibration and scoring using item response theory models with SAS and proprietary software

Dynamic Leaning Maps Project at the Center for Educational Testing and Evaluation⋄ Wrote R functions and scripts to complete statistical simulations, evaluate model-data fit, and analyze empirical data in support of theoretical research about Bayesian Networks and Diagnostic Classification Models⋄ Joined data sources, ensured data quality, and analyzed assessment data using methods such as ANOVA and regression for psychometric score reporting

⋄ Performed data preparation and exploratory/confirmatory factor analyses in support of heterogeneous variance classification modeling of assessment data⋄ Taught Research Methods and General Linear Models Lab sections (PSYC 350)

Attention & Decision Making Lab, Psychology Department⋄ Joined, cleaned, and analyzed data using SPSS for correlational and causal psychological research studies for presentations and publication⋄ Designed research studies, wrote study protocols and IRB applications, submitted and reviewed grant applications, and collected and analyzed survey data⋄ Provided technical and professional mentorship and training to lab members regarding tools for data entry and collection, lab protocols, and research design