Jinyan Lyu: A Professional Data Scientist And Specialist In Machine Learning
Jinyan Lyu: A Professional Data Scientist And Specialist In Machine Learning
Jinyan Lyu is a professional data scientist and specialist in machine learning and applied statistics. Her expertise focuses on transforming complex data into accurate and reliable data-driven models, enabling businesses to make informed, data-driven decisions in the real world. That is what she has done for over five years since graduating from Columbia University with an MA degree in Statistics.
Artificial intelligence (AI) and machine learning technologies are evolving in multiple industries worldwide. Few professionals have the same level of skills and experience as Lyu when it comes to utilizing AI technologies to benefit organizations and society. Her impressive experience includes working at a leading U.S. bank and a major healthcare provider, where she served as their leading data scientist and decision-maker.
Lyu’s primary objective is to help various industries leverage the power of machine learning and data-driven technologies to enhance their decision-making processes for the better. She has already written five peer-reviewed papers that showcase her precision and knowledge in model evaluation, feature engineering, and study design.
Her background shows a history of building and operating machine learning systems at scale to help detect fraud and better manage credit lines for financial institutions. When Lyu worked in the healthcare field, she developed machine learning pipelines using electronic health record (EHR) data to support clinical analytics and evaluate time-to-event models, thereby improving the prediction of outcomes.
An Academic Background in Statistics and Analytics
Lyu has always been interested in statistics and analytics. Her academic background has been essential in propelling her professional career. After completing her Master’s Degree in Statistics at Columbia University, Lyu participated in several notable research studies and applied statistics projects that utilized her quantitative skills. Some of Lyu’s most notable research publications include“Use of Artificial Intelligence for Predicting COVID-19 Outcomes (2022),” “Telemonitoring of Home-Based Biking Experience: Assessment of Wireless Interfaces (2022),”and“Machine learning approaches for exercise exertion level classification using data from wearable physiologic monitors.”
Lyu’s research combines diverse topics, including AI, big data, and health outcomes, to make valid predictions. Much of her health-based data research revolved around predicting COVID-19 outcomes. Since the coronavirus remains largely unknown and unpredictable, AI and data analytics are essential for predicting outcomes such as patient mortality and survival. She also applied these research skills to predict how COVID-19 would impact individuals with pre-existing conditions like asthma.
As a result, Lyu’s research on COVID-19 helped patients during the pandemic. In fact, her research on the mortality rate of asthma patients inspired other medical researchers to discover a cure for COVID-19. Lyu’s data research potentially contributed to saving the lives of thousands of patients with asthma who were infected.
Exercise data was another area of study that interested Lyu. She examined how machine learning technology can interpret the physiological signals generated by wearable health monitoring devices during exercise. Applying AI-powered data technology to modern domains is a testament to the versatility of her skillset.
Core Technical Skills
Lyu needed to acquire many technical skills and programming languages to become proficient in machine learning and data analytics. Her proficiency in Python, SQL, PySpark, NumPy, R, and scikit-learn prepared her for working in cloud environments, such as Apache Spark, Google BigQuery, and Amazon Redshift.
Lyu has utilized her skills to participate in several notable projects throughout her career. These projects include the following:
Participating in each project required Lyu to identify practical problems, design the right modeling framework to address the issues, and then integrate the necessary solutions into the organization.
Professional Career
Lyu began her professional career in the healthcare sector as a data scientist in New York City in 2021. Her job responsibilities included developing machine learning and ETL pipelines on EHR data to help produce accurate clinical analytical data. Then, in collaboration with physicians and medical staff, she could better define outcome features and evaluate time-to-event predictive models more efficiently. This role demonstrated Lyu’s ability to explain complex analytical data to non-technical stakeholders. After all, many stakeholders in the healthcare sector lack a technical background. It is truly helpful for someone with technical skills, like Lyu, to explain the analytical data in a way that makes it easily understandable.
Lyu is an ambitious young professional, to put it mildly. After working for over a year on a health system in New York, Lyu transitioned to work for a financial institution to assess the risks of credit card fraud. She was able to utilize her analytics experience from the healthcare sector to succeed in her new job in the financial services sector. Lyu joined the fraud risk team at a leading U.S. bank. Her major accomplishment was developing machine learning models that could detect fraudulent credit card transactions in real-time. If the AI system identified a suspicious transaction as it occurred, it would immediately step in to block the transaction until the cardholder verified it with the bank.
Lyu transitioned into a job at another financial institution in April 2025, this time in McLean, Virginia, rather than New York. She led production monitoring for the CLIP valuation model and published weekly dashboards and triage playbooks to stabilize performance. She also rebuilt the next-generation CIP model by combining machine learning methods with finance constraints, such as loss/ROA targets and elasticity-aware features.
What’s Next
Jinyan (Andrea) Lyu is not giving up on her education. Even though she continues to work in the financial sector, Lyu is currently attending Trinity University to pursue her Master of Science Degree in Business Analytics. Her expected graduation date is 2026. Enhancing her proficiency in business analytics will equip her with the additional skills necessary to work for major corporations in the business world. Who knows where she might work next as a data scientist?
