I am a data and analytics professional with experience spanning data engineering, operations analytics, business intelligence, machine learning, workflow automation, and product strategy.

I recently completed Duke University's Master of Quantitative Management: Business Analytics program. Before Duke, I worked at Uber Taiwan and VisualSoft, where I used data to improve operational performance, automate reporting, build predictive solutions, and translate complex business questions into practical tools.

My work sits at the intersection of:

• Data Engineering: ETL/ELT workflows, data transformation, validation, reporting pipelines, and automation
• Analytics Engineering: reusable datasets, KPI logic, dashboards, and business-ready data models
• Business Analytics: root-cause analysis, experimentation, forecasting, operational metrics, and strategic recommendations
• Applied AI: AI agents, LLM workflows, recommendation systems, RAG concepts, and human-in-the-loop quality control
• Cross-Functional Execution: partnering with operations, product, sales, CRM, legal, vendors, and executive stakeholders

I am especially interested in roles where I can combine technical execution, business judgment, and clear communication to build reliable data systems that help teams make better decisions.

An AI-powered career concierge designed to help job seekers understand what a job description is really asking for and connect each requirement to evidence from their own experience.

Job seekers often receive polished AI-generated answers but still struggle to understand:

• What the hiring manager actually values
• Which requirements are critical versus optional
• How to prove fit with credible evidence
• Where their experience gaps are

CareerProof transforms unstructured job descriptions into structured, evidence-based interview strategy.

1. Ingest and parse a job description
2. Extract responsibilities, qualifications, tools, and business context
3. Categorize requirements into technical, analytical, operational, and behavioral competencies
4. Match requirements to the candidate's experience and quantified outcomes
5. Generate interview priorities, proof points, skill gaps, and follow-up questions
6. Apply quality checks to reduce unsupported claims and keep recommendations evidence-based

Python LLM Workflows AI Agents Prompt Engineering Structured Output Human-in-the-Loop Evaluation Privacy-by-Design

• Product thinking
• Unstructured-to-structured data transformation
• AI workflow design
• Quality control and evaluation
• User-centered problem solving

An interactive decision-support application that helps operators test how demand shocks, transportation costs, bottlenecks, and inventory constraints affect profitability and service levels.

Operational teams often make planning decisions using static spreadsheets that do not clearly show trade-offs across cost, inventory, capacity, and service.

Built a scenario-planning engine that allows users to adjust business assumptions and immediately compare operational outcomes.

• Demand and supply scenario simulation
• Inventory and capacity constraints
• Transportation-cost sensitivity
• Profit and service-level trade-offs
• Optimization of allocation and operational decisions

Python Pandas PuLP Streamlit Plotly Optimization Modeling

• Makes operational trade-offs visible
• Supports faster scenario comparison
• Converts quantitative models into a usable business interface
• Helps decision-makers move from intuition to evidence

A data and AI solution for analyzing insurance products, customer needs, brand perception, and product recommendation opportunities.

• Processed and structured large-scale insurance and consumer datasets
• Worked with more than 20,000 labeled records
• Supported analysis across approximately 50,000 insurance-related observations
• Developed business-facing outputs around product ranking, customer profiles, sentiment, and demand

• Topic modeling with Latent Dirichlet Allocation
• Recommendation logic using Gradient Boosting Trees
• Feature engineering and labeled-data preparation
• Product and customer segmentation
• Dashboard-based delivery of business insights

Python SQL Pandas Machine Learning Power BI Data Labeling Recommendation Systems

Research from this work was presented orally at IEEE ICBDA 2024 in Tokyo.

A customer and operations analytics project focused on identifying the drivers of flight delays and passenger satisfaction.

• Cleaned and explored airline operational and customer-experience data
• Examined delay patterns across time, route, and service variables
• Conducted feature engineering and exploratory analysis
• Translated findings into operational and customer-experience recommendations

Python Pandas NumPy Matplotlib Data Visualization Exploratory Data Analysis

• Which delay factors have the strongest relationship with customer satisfaction?
• Where are operational bottlenecks concentrated?
• Which improvements could create the greatest customer impact?

A Python-based search and analytics application designed to improve how users review, filter, and interpret large sets of text-based records.

• Keyword filtering and ranking
• Structured data preparation
• Interactive visualization
• Faster comparison across records
• Reusable functions for search and aggregation

Python Pandas Streamlit Plotly

Improved search and review efficiency by approximately 30%.

VisualSoft is an IT solutions company focused on digital transformation, workflow improvement, analytics, and AI-enabled business solutions.

• Built Python-based workflows to extract, clean, transform, and validate business datasets
• Automated recurring data preparation and reporting processes, reducing processing time by 50%
• Developed Power BI dashboards integrating multiple data sources, improving decision efficiency by 30%
• Structured and analyzed 100K+ insurance records and 400K+ cross-industry data points
• Supported predictive models and data products used in patent-related initiatives
• Researched LLM and generative-AI applications for business process improvement
• Worked with clients and cross-functional teams to translate business needs into technical workflows

• Batch ETL-style workflows
• Data transformation and validation
• Reporting pipelines
• Reusable Python scripts
• Dashboard-ready data models
• Client-facing requirements gathering

• Used operational, ERP, inventory, and customer-feedback data to support store and fulfillment decisions
• Reduced defect and complaint rate from 5% to 1%
• Expanded SKU capacity from 1,000 to 3,000
• Improved same-day delivery performance by approximately 30%
• Supported Oracle ERP and legacy-system implementation
• Built automated reporting and communication workflows using Excel, VBA, chatbots, and dashboards
• Partnered with Product, Sales, CRM, PR, Legal, operations teams, and vendors across Taiwan, China, and the United States
• Led or supported five new product and operational initiatives

• Inventory visibility
• Product-capacity planning
• Customer complaint analysis
• Defect root-cause identification
• Manual reporting inefficiency
• Cross-functional execution

• Analyzed campaign and audience-performance data
• Improved click-through rate by 50%
• Improved paid-click efficiency by 200%
• Connected content, audience, and acquisition metrics to editorial and marketing decisions

Excel Digital Analytics Campaign Metrics Audience Segmentation Performance Reporting

• Coordinated a volunteer community of approximately 100 contributors
• Organized 8 workshops across beginner, web-scraping, and advanced Python topics
• Supported more than 100 participants
• Increased program completion by approximately 30% through LinkedIn certificates and structured engagement
• Increased community reach by more than 2,200%
• Built workshop, instructor-review, volunteer, survey, and event SOPs
• Used feedback data to identify future workshop topics and improve program design

Program Management Community Analytics Survey Analysis Stakeholder Coordination Process Design Technical Education

• Supported speaker coordination, promotion, and event operations
• Helped build chatbot-enabled communication workflows
• Contributed to social campaigns reaching approximately 50,000 people and generating around 5,000 engagements

I actively support inclusive technical education, especially initiatives that help women and career changers build confidence in Python, analytics, and data-driven problem solving.

S3 IAM EC2 Lambda RDS Redshift Glue Athena CloudWatch EMR Kinesis SQS SNS EventBridge Step Functions QuickSight

ETL/ELT Data Pipelines Data Modeling Data Warehousing Data Quality Metadata Data Lineage Orchestration Incremental Loads Batch Processing Streaming Concepts

AI Agents LLM Workflows RAG Concepts Embeddings Vector Stores Prompt Engineering Structured Outputs LangChain Concepts LangGraph Concepts Evaluation Human-in-the-Loop Design

I recently completed the 5-Day AI Agents Intensive with Google and Kaggle, where I explored agent workflows, tool use, spec-driven development, evaluation, safety, privacy, and production readiness.

Master of Quantitative Management: Business Analytics
Strategy Track | 2026

Focus areas:

• Business analytics
• SQL and data management
• Statistics and decision analytics
• Machine learning
• Data visualization
• Business and platform strategy

Bachelor's Degree — Political Science, International Relations

Additional technical coursework:

• Machine Learning
• Java
• C/C++ Data Structures and Algorithms
• Linear Algebra
• Discrete Mathematics
• Information Security
• Database and programming fundamentals

• IEEE ICBDA 2024 Oral Presentation — Insurance product recommendation using topic modeling and gradient boosting
• Built data and predictive solutions supporting patent-related initiatives
• Completed the Google & Kaggle 5-Day AI Agents Intensive
• Ongoing AWS Cloud Practitioner preparation

I am currently strengthening my skills in:

• Cloud-based data pipelines on AWS
• Data warehousing with Snowflake and Redshift
• Pipeline orchestration with Airflow
• Distributed processing with Spark / PySpark
• Streaming architecture with Kafka and Kinesis
• Analytics engineering with dbt
• Production-ready AI agent and RAG workflows

I am especially interested in opportunities across:

Data Engineering Analytics Engineering Business Intelligence Product Analytics Operations Analytics Applied AI Business Systems Analytics

A dashboard is only as reliable as the pipeline behind it.
A model is only as valuable as the decision it improves.
A technical solution only matters when people can understand, trust, and use it.

My goal is to build systems that are not only technically sound, but also usable, explainable, and connected to measurable business outcomes.

I am always happy to connect with people working in data engineering, analytics, cloud technology, applied AI, operations, and inclusive technical education.

• LinkedIn: Alice Chen
• Email: wy.alice.chen@gmail.com
• Location: Durham, North Carolina, United States