Jun Yan work email
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Jun Yan personal email
Jun Yan is a Staff Scientist at Uber. They possess expertise in python, machine learning, data analysis, scientific computing, simulations and 18 more skills. They is proficient in Danish. Colleagues describe them as "Jun was my summer internship mentor at AT&T. She is an energetic, brilliant, and experienced data scientist, helping me a lot in my professional as well as personal development. Specifically, I’m so impressed by what she has done in the following aspects. 1. Responsibility and patience. On my first day, Jun showed my summer projects including several stages, corresponding timeline, and study materials. The clear logic and good preparation she made before I came really plays an important role in the final success of my internship. 2. Efficiency. Admittedly, Jun is one of the most efficient people I’ve ever met. We have at least two meetings each week. Each time she provides quick feedback to the project progress. Furthermore, she shares with me some good tips on how to increase efficiency not only in our project but in general as well. 3. Business sense. Besides lots of technical skills, she’s also good at translating an open-ended business problem into a data science question. Because of her good business version, our projects impressed the data science team as well as the leadership team. In conclusion, Jun is a great data scientist mentor, helping me equipped with not only advanced big data skills but also deeper enthusiasm about data science. I highly recommend her!"
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Staff ScientistUberCalifornia, United States -
Manager Ii, Applied ScienceUber Mar 2022 - PresentSan Francisco, California, Us2022 Oct - now: Uber Ads2022 April- Sept: UberAI, uMetric
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Senior Manager, Data ScienceStitch Fix Jul 2021 - Mar 2022San Francisco, Ca, UsShop Recommendation, Personalization and Ranking -
Director, Data ScienceDemandbase Jun 2020 - Jun 2021San Francisco, Ca, UsI lead a central data science team at Demandbase. Our team is responsible for all DS initiatives and projects in Demandbase. We work cross functionally with UI/UX designers, product managers, project managers, data engineers, application developers, devops and custom success managers to deliver the best account based experience using data, ML and AI. We are currently working in three areas: - Build new predictive analytics features for the DB1 platform. The Qualification and Pipeline Predict scores were build with machine learning algorithms and deployed to hundreds of our customers. - Improve traffic identification of mapping hundreds of billions of IPs and cookies to companies. - Improve B2B advertising performance and deliver new B2B advertising products. -
Data Science ManagerDemandbase Aug 2019 - Jun 2020San Francisco, Ca, Us• Lead the Data Science effort in B2B advertising team. My team works cross functional with product/design, data engineering and application and built inventory forecasting, audience segments, click through rate (CTR) model, engagement model, pacing and pricing strategy, experimentation and AB test, intent model; as well as designed campaign KPIs and built performance dashboards. • Built a scalable v2 version of CTR model and resulted in 70% increase in CTR and percentage of poor performing campaigns dropped from 25% (v1 model) to 3% (v2 model). • Introduced deep learning models for real time contextual targeting. Resulted in substantial improvements in targeted page relevance to our customers.
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Principal Data ScientistDemandbase Jan 2019 - Aug 2019San Francisco, Ca, Us• Built collaborative filtering algorithm to identify and target high intent buyers from the 150 billion B2B intent signals every month, and resulted in 30X ROI comparing to other ads agency.• Implemented click through rate (CTR) model for real time bidding (RTB) in Golang. • Developed and implemented Pricing strategies for targeting.• Built data visualization and dashboards for KPIs for monitoring purposes and for consuming by other stakeholders. • Built monitoring and alert system for the ML products I built. • Tech stack used : Apache Airflow for data pipeline and job scheduling; Google Bigquery for data ETL; Jyputer Notebook, Scikit-learn, Pandas, Matplotlib for model prototyping with small sampled data; Spark MLLib, PySpark, Qubole and Amazon S3 for model prototyping for large data, Scala and Google DataProc for productize the machine learning algorithms, Google Datastudio for building and monitoring KPIs, Grafana for monitoring and alerting, Kibana and NewRelic for logs and trouble shooting.
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Senior Data ScientistDemandbase Oct 2017 - Dec 2018San Francisco, Ca, Us• Developed algorithms to rank keywords by their relevance to customer products and implemented the solution as Keyword Relevance API. This API powers all Demandbase AI Solutions including ABM Platform, Targeting, Conversion and Real Time Intent.• Increased relevance from 68% to 98% for displayed webpages using natural language processing techniques matching billions of webpages with keywords.• Built random forest classifier on Spark with 98.5% precision to binary-classify 300 million urls.
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Senior Data ScientistAt&T Oct 2014 - Oct 2017Dallas, Tx, Us• Predicted customer churn with web click logs using logistic regression and gradient boosting tree.• Analyzed mobile data usage pattern and used them to predict demographics. • Identified influencers from communication social network of hundreds of millions of users and billions of edges using pyspark, python and various graph analysis tools (graphX, SNAP etc). • Extracted patterns for customer call reasons using association rule mining.• Generated social tags using tens of billions of tweets for 17000 TV shows at scale with pyspark; analyzed correlation between social activities and TV viewership; built topic graphs for TV shows.• Initialized bottom-up data science projects (include expert finder, employee timeline, project recommender and internal news engine), got approved by VP and led a team to implement them using various NLP techniques including topic modeling, keywords extraction and text summarization. • Supervised two interns to perform community detection in large scale social network and implement news recommendations using LDA and word2vec. -
Research Assistant ProfessorColorado School Of Mines Mar 2014 - Sep 2014Golden, Co, Us• High throughput computation and data mining on thermoelectric materials.• Predictive cohesive energies using machine learning. I used various algorithms including kernel ridge regression, support vector regression and k-nearest neighbor, etc. to predict cohesive energies of solids on a dataset of over 60,000 calculations and ~9000 materials. The prediction error is reduced to 85 meV/atom, twice lower than previous record. -
FellowInsight Data Science Jan 2014 - Feb 2014San Francisco, Ca, Us• Created newsline.me, a web app to provide context for news in the form of a timeline.• Utilized Google and New York Times APIs to retrieve news articles in real time.• Filtered urls, tokenized and removed stopwords from the news, and stored the data in MySQL.• Applied machine learning (unsupervised clustering) with scikit-learn using TF-IDF (term-frequency inverse document frequency) and n-grams algorithms.• Visualized the timeline and the feature space of the unsupervised clustering using jQuery and D3.• Deployed an interactive web app using Flask, Twitter Bootstrap. Hosted the app on AWS. -
Research AssociateStanford University Sep 2011 - Feb 2014Stanford, Ca, Us• Designed an automated pipeline using Python, Bash and Matplotlib to build structural models, submit simulations to supercomputers, collect data, visualize, and analyze results with regression models.• Mined semi-structured numerical data involving electronic and magnetic properties of metal oxides and hydroxides using Python and SQLite, analyzed the trend using d-band model and identified correlations between different structures.• Improved the simulation time for chemical reactions of molecules on surfaces 40x by porting Python/C code to GPUs : https://trac.fysik.dtu.dk/projects/gpaw/browser/branches/rpa-gpu-expt• Diagnosed and resolved system wide intermittent crashes in collaboration with interdisciplinaryengineers.
Jun Yan Skills
Jun Yan Education Details
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Stanford UniversitySlac -
Institute Of Physics, Chinese Academy Of SciencesPhysics
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Wuhan UniversityPhysics
Frequently Asked Questions about Jun Yan
What company does Jun Yan work for?
Jun Yan works for Uber
What is Jun Yan's role at the current company?
Jun Yan's current role is Staff Scientist.
What is Jun Yan's email address?
Jun Yan's email address is jy****@****fix.com
What schools did Jun Yan attend?
Jun Yan attended Stanford University, Institute Of Physics, Chinese Academy Of Sciences, Wuhan University.
What skills is Jun Yan known for?
Jun Yan has skills like Python, Machine Learning, Data Analysis, Scientific Computing, Simulations, R, Mysql, Numpy, C, Gpu, Github, Statistics.
Who are Jun Yan's colleagues?
Jun Yan's colleagues are Ross Gullo, Rafael Barrios, Lucas Antonio, Ariba Zaidi, Christopher Gonzalez, Paulo Torres Mora, Allan Moraes.
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