Data Science & Analytics Professional Strategy Leader

BRIANA LOVE WHYLIE (She/Her)

New York, NY ***** ******.*.******@****.*** 646-***-****

Versatile professional with hands-on experience in labor unions, nonprofit sector, and corporate settings, having contributed to financial operations in entertainment industry. Currently a master’s candidate in a Data Science Program, skilled at research, analytics, problem-solving and bringing a well-rounded perspective and strong work ethic to every team.

EDUCATION

Pace University, Seidenberg School of Computer Science and Information Systems New York, NY

Master of Science (MS) in Data Science Concentration: Quantitative Research and Data Science GPA:4.0 August 2026

Courses: Programming for Data Science, Machine Learning, Predictive Analytics

Berkeley College, New York, NY B.B.A in International Business Concentration: Finance GPA: 3.6 December 2018

TECHNICAL SKILLS

Programming Languages: Python, R, Java, C++, SQL

Database Management: MySQL, PostgreSQL, Oracle Database, Qualtrics

Data Visualization Tools: Power Bl, Matplotlib, Seaborn, Tableau

Libraries: Pandas, NumPy

PROFESSIONAL WORK EXPERIENCE

Seentio Start-up Financial Software Data Scientist Apprentice Finance Department March 2026 -Present

●Analyze dividend-focused financial data to identify the key metrics investors use to evaluate income, safety, growth, and overall portfolio performance.

●Build, design, and support data-driven tools and dashboards that help display dividend yield, payout ratio, dividend growth, cash flow coverage, and related investment metrics in a clear, user-friendly way.

●Write and refine code in Python and SQL for data cleaning, analysis, metric calculation, and workflow automation.

●Assist in designing and improving internal programs, data pipelines, and dashboard features that support investor decision-making.

●Collaborate with team members on product, data, and design tasks to create tools that make complex financial information more actionable for investors.

Universal Music Group Accounts Payable Finance Department March 2019-March 2020

●Managed artist financials, boosting accuracy and efficiency.

●Led complex artist contract administration and financial reconciliation processes, implementing new tracking systems for enhanced accountability.

●Processed high volumes of invoices across multiple ERP systems, ensuring proper coding and authorization.

● Resolved invoice issues and credit memos by coordinating with suppliers and internal departments.

●Reconciled monthly vendor statements and maintained detailed payment records.

●Managed incoming inquiries via phone and email in a professional, timely manner.

●Updated job procedures quarterly and communicated changes to management.

●Maintained confidentiality of vendor and financial information with a high level of accuracy.

TECHNICAL PROJECTS

Python Data Processing Script February 2025

●Debugged & refactored legacy code, resulting in 40% improvement in runtime performance & increased accuracy of output reports.

●Automated data cleaning & transformation tasks with Python, reducing processing time from 4 hours to under 20 minutes per dataset, applying object-oriented programming principles to build modular, reusable code for scalable future use.

●Leveraged Pandas & NumPy for data analysis, generating actionable insights to support decision-making.

●Refactored legacy code, improving runtime performance by 40% & increasing output accuracy.

Dictionary AI Chatbot November 2025

●Designed and built an AI-powered dictionary chatbot that answers definition-based queries (e.g., definitions, parts of speech, synonyms, usage examples) in a clear, structured format.

●Collected, cleaned, and standardized lexical data into a structured dataset to ensure consistent, accurate responses.

●Implemented semantic search / retrieval (embeddings + ranked lookup) so the chatbot pulls the most relevant entries before generating an answer (reducing incorrect outputs).

●Developed prompt/response logic for formatting (bulleted definitions, examples, synonym lists) and added fallback handling when confidence or matches are low.

VIRTUAL WORK SIMULATIONS

British Airways Data Science Job Simulation The Forage April 2025

●Automated ETL pipeline to clean & process 10k+ customer reviews using Python (Pandas, NumPy), preparing data for sentiment-based modeling.

●Performed EDA & visualizations (Matplotlib, Seaborn) to uncover trends in customer satisfaction & purchase behavior, building logistic regression model with 82% accuracy to predict purchase likelihood based on sentiment analysis.

●Delivered data-driven insights that informed retention strategies & supported targeted customer engagement recommendations.

JPMorgan Chase & Co. Quantitative Research Virtual Experience Program The Forage April 2025

●Created Python-based pricing model to automate commodity contract valuation, cutting manual processing time from 45 to under 5 minutes.

●Built logistic regression model to estimate borrower default probability using key loan features from 50k+ row dataset, applying feature engineering & credit profile categorization to improve model accuracy by 20% over baseline.

●Developed reusable analytics workflows to support future risk assessment & financial forecasting initiatives.

LEADERSHIP

Peace Boat United Nations, Youth Ambassador September 2022– Present

●Advocate for sustainable development initiatives during UN Panels through research-backed speeches, leading youth-focused initiatives through partnerships with global investors to seek project funding.

INTERESTS

Muay Thai Weightlifting Welding Art Golfing

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