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Principal Applied ML Science Lead @ Microsoft | Ex-Amazon | Duke | PhD
I’m an experienced technical team lead with a strong track record of leading, developing, and motivating teams to achieve success. Experienced with providing technical guidance and mentorship to team members, helping them grow their technical skills and knowledge and successfully deliver results. Skilled at adapting coaching approaches in changing business needs and priorities and motivating team members to achieve success. Strong communication skills and ability to build positive relationships with team members and stakeholders. Demonstrated success in setting clear goals and expectations while providing constructive feedback to help team members build confidence and meet business objectives. I obtained my Ph.D. in Computational Biology and Bioinformatics, and received doctoral training in Statistics and Machine learning. I have successfully applied my technical expertise to varied domains such as fraud detection, cloud computing, AIOps, cloud data center operation, biomedical/pharmaceutical fields and clinical trials, helping organizations to achieve substantial cost savings. Specialty High dimensional predictive modeling, Bayesian statistics, Bayesian sparse factor analysis, statistical machine learning, data mining, feature selection, disparate data fusion, statistical computing. Machine learning and Statistics: 1) Supervised and unsupervised machine learning methods. 2) Multivariate data dimension reduction: PCA, Factor analysis(including nonparametric Bayesian FA with beta-Bernoulli process). 3) Regularization methods: (Bayesian and frequentist)-Lasso, Ridge, Elastic net. 4) Statistical methods on linear models, generalized linear models; survival analysis, shared frailty model,etc. 5) Statistical inference/computing: MCMC(Metropolis-Hastings, Gibbs sampling), variational Bayesian, EM, MAP, Gradient descent. 6) Computational algorithm design. Statistic
Zhejiang University
B.S, Biotechnology
N/A – Present
Duke University
PhD, Computational Biology & Bioinformatics
N/A – Present
North Carolina State University
visiting student, Bioinformatics Research Center,Prof. Zhao-Bang Zeng's research group
N/A – Present
Microsoft
Principal Applied Science Leadership, M365 Core
January 1, 2023 – Present
Microsoft
Principal Applied ML Scientist Lead, Cloud + AI
March 1, 2019 – January 1, 2023
Amazon
Applied Scientist II
October 1, 2016 – March 1, 2019
Amazon
Applied Scientist
April 1, 2015 – September 1, 2016
PPD
Biostatistician
November 1, 2013 – March 1, 2015
raleigh-durham, north carolina area
Duke University
Research Assistant
August 1, 2008 – October 1, 2013
Durham, NC
June 2023 MLADS Conference Presenter
Microsoft
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in large, research-intensive tech companies (Microsoft, Amazon) and academic institutions (Duke University). Their experience spans diverse problem domains including AIOps, cloud computing, fraud prevention, and bioinformatics, indicating adaptability and a broad intellectual curiosity. The transition from a Biostatistician/Research Assistant to Applied Scientist and then Principal Applied Science Leader demonstrates a growth mindset and ability to take on increasing responsibility and complexity. Their involvement in publications and conferences suggests a collaborative and knowledge-sharing culture fit. The target role of 'Data Analyst' is a significant shift from their recent leadership roles, which might indicate a desire for a more hands-on, analytical focus, or a potential mismatch if the role is not sufficiently challenging for their experience level.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong leadership in applied science, evidenced by driving end-to-end strategies, leading development efforts, and mentoring teams. Their experience in architecting complex systems and establishing evaluation frameworks suggests strong problem-solving and analytical skills. The numerous publications and presentations indicate a proactive approach to knowledge sharing and technical communication. While direct operational fit for a pure Data Analyst role might require a slight adjustment from their Principal Applied Science leadership roles, their foundational data analysis and statistical modeling skills are highly relevant.