Evelyn Siu
Data Scientist
Evelyn Siu is an accomplished data science professional with a diverse background spanning various industries and sectors. Currently, she serves in multiple roles, including Data Scientist at Garner Health, Data and Policy Analyst at the City of Chicago, Data Fellow at the New Hampshire Democratic Coordinated Campaign, Data Scientist at the Centers for Medicare & Medicaid Services, and Civic Digital Fellow at Coding it Forward.
Professional Overview
Evelyn is a skilled data scientist with expertise in leveraging data-driven insights to drive strategic decision-making and operational improvements. Her industry focus encompasses healthcare, public policy, and civic engagement, where she utilizes advanced analytical techniques and data visualization to tackle complex challenges.
Experience Summary
Current Roles
As a Data Scientist at Garner Health, Evelyn is responsible for developing and implementing data-driven solutions to optimize patient outcomes and improve healthcare delivery. She collaborates cross-functionally to analyze large datasets, identify trends and patterns, and translate findings into actionable recommendations.
In her role as a Data and Policy Analyst at the City of Chicago, Evelyn supports data-driven policymaking by conducting comprehensive analyses on municipal data. She leverages her expertise in data processing, statistical modeling, and visualization to inform strategic planning and resource allocation decisions.
Evelyn also serves as a Data Fellow at the New Hampshire Democratic Coordinated Campaign, where she applies her data science skills to support political strategy and community engagement initiatives. She designs and implements data-driven campaigns to drive voter mobilization and political participation.
Additionally, Evelyn works as a Data Scientist at the Centers for Medicare & Medicaid Services, where she contributes to the development and optimization of healthcare programs and policies. She utilizes her analytical prowess to uncover insights that enhance the delivery and accessibility of healthcare services.
As a Civic Digital Fellow at Coding it Forward, Evelyn dedicates her time to advancing civic technology initiatives, leveraging her data science expertise to address societal challenges and promote public good.
Career Progression
Prior to her current roles, Evelyn gained valuable experience as a Data Analyst and Research Assistant, honing her skills in data management, statistical analysis, and problem-solving. Her ability to translate complex data into actionable insights has been instrumental in her career progression and the success of the organizations she has served.
Academic Background
Evelyn holds a Master's degree in Data Science from the University of Chicago, where she specialized in machine learning, predictive modeling, and data visualization. Her academic achievements include Dean's List recognition and the completion of several data-driven capstone projects.
Areas of Expertise
- Data analysis and modeling
- Statistical inference and hypothesis testing
- Machine learning and predictive analytics
- Data visualization and dashboard creation
- Stakeholder communication and data-driven decision-making
- Project management and cross-functional collaboration
Professional Impact
Throughout her career, Evelyn has made significant contributions to various organizations and initiatives. At Garner Health, she developed a predictive model that enhanced patient risk stratification, leading to improved resource allocation and better health outcomes. As a Data and Policy Analyst for the City of Chicago, Evelyn's data-driven insights informed policy decisions that addressed key challenges in public service delivery.
Conclusion
Evelyn Siu is a versatile and impactful data science professional with a proven track record of driving tangible results across diverse industries. Her technical expertise, analytical acumen, and ability to translate data into insights make her a valuable asset to any organization seeking to harness the power of data-driven decision-making.
