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Jingyi He
Data Scientist at Criteo
Professional Background
Jingyi He is a seasoned data scientist with a wealth of knowledge in statistical and machine learning methods. With extensive expertise that spans from predictive modeling to mixed-effects models and A/B testing, Jingyi has proven her capability to derive actionable insights from complex datasets. Her practical experience in parallel programming on enterprise Unix platforms makes her an invaluable asset in today’s data-driven business environment.
Jingyi's career began as an analyst at Zhejiang Yiwu Small Commodity Wholesale Market, where she developed her analytical skills and gained firsthand experience in statistical methodologies. She then moved on to Zhongbang Industrial Development Co., Ltd, further honing her ability to analyze economic data and contribute to strategic decisions.
Later, Jingyi expanded her technical abilities as an intern at Fresenius Medical Care North America, where she further cultivated her versatility in data analysis. This role was crucial in shaping her understanding of how data science can directly impact business performance.
Jingyi's academic pursuits at the University of California, Santa Barbara, where she studied both her Master’s and Ph.D. in Applied Statistics, equipped her with a robust statistical foundation. She later translated this academic prowess into real-world application at Walmart Labs and now as a Data Scientist at Criteo, where she collaborates with cross-functional teams to drive significant improvements in business processes, customer service, and product sales.
Education and Achievements
Jingyi He boasts an impressive educational background with degrees that emphasize Applied Statistics. She holds both a Master’s and Ph.D. from the prestigious University of California, Santa Barbara, where she delved deep into data analysis methodologies and their applications in real-world scenarios. Her undergraduate studies at Zhejiang Gongshang University laid a solid foundation in Economic Statistics, further complemented by her Master’s degree from the same institution.
This educational blend of practical statistical training and economic insight allows Jingyi to approach problems holistically, understanding both the numbers and their broader business implications. She is also a SAS Certified Programmer, which showcases her expertise in using SAS software for advanced statistical analysis.
Skills and Expertise
Jingyi has a diverse technical skill set that is crucial for a data scientist operating in contemporary environments. She is proficient in various programming languages and tools including Python, SQL, R, H2O, Hive, and Hadoop. Her capabilities in statistical inference, regression, classification, and time series analysis make her exceptionally well-equipped to tackle complex data challenges.
Her proficiency in data visualization and experimental design showcases her ability to not only analyze data but present it in a way that is easily understandable and actionable for decision-makers. As a collaborative team player, Jingyi excels at working with cross-functional teams to ensure that data insights effectively drive business strategies.
Notable Accomplishments
Throughout her career, Jingyi has been recognized for her ability to convert theoretical statistical concepts into practical solutions that facilitate enhanced business operations. Her contributions at Criteo have led to improved customer engagement strategies, showcasing her ability to harness data effectively. At Walmart Labs, her analytical insights significantly contributed to product optimization that drove sales growth.
Jingyi's experience as a teaching associate/assistant at the University of California, Santa Barbara underscores her commitment to knowledge-sharing and mentoring the next generation of data scientists. This role not only solidified her own understanding of complex statistical concepts but also allowed her to guide others in their academic journeys.
Key Skills and Technologies
- Programming Languages: Python, SQL, R, H2O, Hive, Hadoop, Excel
- Statistical Techniques: Statistical inference, A/B testing, regression, classification, logistic regression, support vector machines (SVM), random forest, boosting models, gradient boosting (xgboost), clustering, data visualization, experimental design, mixed effects models, time series analysis, survival analysis
- Certifications: SAS Certified Programmer
Location
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