Expert Details
Data Analysis | Data Science | Machine Learning | Algorithms | Mathematics | Artificial Intelligence | Ad-tech |eCommerce | Physics | Memory Development | Leadership
ID: 736354
Illinois, USA
She has ideated and developed predictive model for customer journey to a purchase using Markov chains, Reinforcement Learning. Expert paved the way to profitable marketing across product categories by creating product recommendation model (clickstream). She has 20 years of experience in Data Analysis, Data Science and predictive modeling projects and using advanced mathematical modeling, machine learning techniques. Expert advises both formally and informally leaders of start-ups and emerging technology companies as Subject-matter expert. She has expertise in memory and learning skills development. Expert is a primary author or co-author of more than 20 scientific papers published in major peer-reviewed physics journals and two patents.
Education
| Year | Degree | Subject | Institution |
|---|---|---|---|
| Year: 2017 | Degree: Global Emerging Leader, Certificate | Subject: Manage Mentor Leadership Solutions | Institution: Harvard University |
| Year: 2004 | Degree: Ph.D. | Subject: Physics and Mathematics | Institution: Kirensky Institute of Physics, SB Russian Academy of Sciences |
| Year: 2000 | Degree: M.S. | Subject: Physics and Engineering | Institution: Reshetnev Siberian State Aerospace University |
Work History
| Years | Employer | Title | Department |
|---|---|---|---|
| Years: 2020 to Present | Employer: Undisclosed | Title: Head of Data Science | Department: Data Science and Innnovations |
Responsibilities:• Subject Matter Expert advising in Data Science, Machine Learning, Deep Learning, AI, Ad-tech, eCommerce, Healthcare, Finances.• Algorithmic solutions, quantitative modeling, image processing, behavioral targeting modeling. |
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| Years | Employer | Title | Department |
| Years: 2019 to 2020 | Employer: pEXCHANGE, LLC | Title: Director of Data Science & Artificial Intelligence | Department: |
Responsibilities:• Developing a capability to capitalize on community innovation culture, artificial intelligence, knowledge, distributed problem solving, data gathering, intelligence & analytics and a collaboration platform for STEM, R&D, workforce and technology transition (iDispla.com).• Building large scale business intelligence applications such as analytical frameworks and budgeting software. • Directly work with key client stakeholders to define business problems and determine solution requirements. • Using statistics, DS, AI, and advanced analytics enabling enterprises to generate business value from data. • Developed ML semantic match using NLP: sem. similarities, NLTK/Gensim, GloVe/Word2Vec/BERT. • Managing innovative DS and AI projects focused on seeking solutions to real-world problems. • Collaborating with agile teams of full stack engineers. |
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| Years | Employer | Title | Department |
| Years: 2020 to Present | Employer: Undisclosed | Title: Subject-matter expert - Assessor | Department: |
Responsibilities:Program: Machine Learning for managers (part time teaching) |
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| Years | Employer | Title | Department |
| Years: 2016 to 2019 | Employer: W.W. GRAINGER, Inc., Lake Forest, IL | Title: Sr. Data Scientist-Lead, Marketing, eCommerce/Business Insights, Artificial Intelligence | Department: Data Science and Analytics |
Responsibilities:• People and projects management; interviewing people; working with business partners and intellectual property team.• Accomplished the Global Emerging Leader program (Harvard University, Manage Mentor Leadership Solutions). • Drove profitable investment strategies by developing predictive model for Customer acquisition and marketing. • Ideated and developed predictive model for Customer journey to a purchase using Markov chains, Reinforcement Learning. • Paved the way to profitable marketing across product categories by creating product recommendation model (clickstream). • Developed semantic match model using NLP, NN, Machine & Deep Learning, Word2/Doc2Vec, Semantic Similarities. • Dramatically improved a product recommendation engine by developing a model for predicting risk of Revenue decline. • Developed association rules for up- and cross-selling based on viewed and purchased products (clickstream data) that significantly improved customer experience on www.grainger.com. • Modeled negotiation outcome (Cost Support); developed algorithm for Sentiment Analysis of surveys. • Derived a new method based on SVD for working with sparse matrices; developed Web scraping code. • Programming/code mgt.: Python/SPARK, R, Excel VBA, Git/BitBucket, Shiny/Theano/Keras,/TensorFlow, Tableau. • Dev. env.: Windows/Mac/Unix, GPU, PyCharm, PyTorch, H2O, VSC, R Server; Data: Teradata, SQL, Hadoop. |
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| Years | Employer | Title | Department |
| Years: 2014 to 2016 | Employer: CENTRO, Inc., Chicago, IL | Title: Predictive Analytics Data Scientist | Department: Data Science |
Responsibilities:• Established the scope of predictive analytics development for Data Science (DS) team. Have been extensively and actively involved into hiring process: profile selection, evaluation of the pre-interview case study tasks, interviews, decision making. Have grown DS group from 2 to 5 people; hired three Ph.D. Data Scientists.• Leveraging data science to maximize financial performance & boost ROI for social advertising campaigns at Centro and Facebook portfolios. Periodical reports and discussion of results and prioritizations with business leadership. • Developed advertiser-publisher scoring system and automotive recommendation system for a programmable media platform by applying CF, SVD, iSVD, LFM, and Markov chains that boosted recommendation quality. • Developed Ads Optimization, click prediction, and audience models for online advertising using Centro's and external data, optimization, clustering and classification algorithms. • Performed Quantitative Research, Predictive Statistical Modeling, Model Validation, including Linear Mixed-Effects, Uni-/multivariate and Factor Analyses, Logistic Regression, Random Forest, Collaborative Filtering, Neural Networks, Real Time Analytics, Web Analytics. • Analysis of user cookie-level data and Real-time bidding data (multi-TB scale) using Amazon S3, Hadoop cluster with Spark, Spark MLLib, Hive and Python. • Found new modeling approaches for Real-time bidding via programmatic instantaneous auction, similar to financial markets, to know problems and realize it as a software tool that paved the way to profitable click predictions. • Performed time series analysis, dynamic linear modeling, content-based recommendation, value-added models, and probabilistic record linkage involving Big Data and Machine Learning, Bayesian statistics, probabilities, Markov chain Monte Carlo techniques. • Databases/processing: SQL, Pentaho, PostgreSQL, NoSQL, Neo4j, Mongo, Hive, Cassandra, Hadoop. • BigData processing with Hadoop, HDFS, programming using Spark, MapReduce, Pig language. • Programmed: Python, SPARK, R, C#, Excel VBA, Cypher, Matlab. • Agile development environments: Windows, UNIX, Mac. |
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| Years | Employer | Title | Department |
| Years: 2013 to 2014 | Employer: CME GROUP, Inc., Chicago, IL | Title: Sr. Risk Management Analyst | Department: Clearing House |
Responsibilities:• Performed risk measurement using techniques: Greeks, VaR, HVaR, Expected Shortfall, Tail Analysis, Scenario Analyses, Stress testing. Knowledge of stress scenarios for use in economic capital methodology and stress testing (CCAR).• Worked with Commodities, Fixed Income, OTC, IRS, CDS, Futures/Options, Swaps, FX, Equities, Cross-Margin accounts. • Conducted current margin rate analysis, new product margin determination, and volatility database maintenance. • Assessed current market risks and price movements; maintained high standard of coverage while preserving capital efficiency throughout the CME complex. • Worked closely with DevOps on the development of the application for real time trading activity analysis. • Developed decision logic supporting risk management and business initiatives, project management. • Performed detailed analysis on investment products, strategies and portfolios across asset classes. • Conducted modeling and statistical analysis for decision making support. • Used SPAN, Margin Analysis, Volatility DB, Calypso, and other software applications. • Participated in the development cycle of the Real Time Market Risk dashboard. • Conducted UAT testing, programmed in Excel VBA, MatLab, C#, R, and SQL. |
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| Years | Employer | Title | Department |
| Years: 2012 to 2013 | Employer: CME Group | Title: Quantitative Risk Research Consultant | Department: Clearing House |
Responsibilities:• Developed a prototype of margin model using market risk methodologies: SPAN, VaR, Historical VaR, Expected Shortfall (CVaR) and tail risk.• Developed approach for modeling the Energy Risk, Liquidity risk of Commodities derivatives; GARCH, EWMA, ARMA models. • Performed time series analysis, statistical analysis and seasonality analysis of historical and current market data. • Worked with Options/Futures, Energy, Equity, FX, Metals, Agriculture commodities and their derivatives. • Worked with IT group to improve data quality for regulatory and modeling purposes; Prototyping. • Conducted analytical research for the purpose of modeling and forecasting financial data. • Extensive programming in Matlab, Excel VBA, R, C#, and SQL. |
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Career Accomplishments
| Associations / Societies |
|---|
| Big Data TechCon participation, Hands-On H2O Workshop and H2O community, AI Innovation Summit 2019 (invited), All kind of workshops like Product Recommendation Engine for Direct Sales. Grainger, Lake Forest. IL (2017), Meetup workshops, Passively on Kaggle, The Caltech-JPL Summer School on Big Data Analytics, Used to serve as Subject-matter expert at UC BERKELEY, School of Information (Program: Machine Learning for managers), Coursera and Udemy community, Github community, Stackoverflow. |
| Licenses / Certifications |
|---|
| Big Data TechCon, Certificate of Completion (San Francisco, 2014) The Caltech-JPL Summer School on Big Data Analytics (Caltech) Machine Learning (Stanford University, 2014) Global Emerging Leader program (Harvard University, Manage Mentor Leadership Solutions) |
| Publications and Patents Summary |
|---|
| She is a primary author or co-author of more than 20 scientific papers published in major peer-reviewed physics journals, 1 granted and 1 pending patent. |
Additional Experience
| Training / Seminars |
|---|
| • Natural Language Processing Specialization by DeepLearning.AI • Artificial Intelligence A-Z™: Learn How To Build An AI • Natural Language Processing with Deep Learning in Python • Deep Learning and Tensorflow (Google`s Deep Learning Frameworks) • Deep Learning A-Z™: Hands-On Artificial Neural Networks. • Artificial Intelligence A-Z™: Learn How to Build An AI. • Reinforcement Learning (Udacity) • Big Data TechCon, Certificate of Completion (San Francisco, 2014) • The Caltech-JPL Summer School on Big Data Analytics (Caltech) • Process Mining: Data science in Action (University of Technology) • Programming for Everybody (Python) (University of Michigan) • Mining Massive Datasets (Stanford University) • Machine Learning (Stanford University) • Statistics (Princeton University) • Web Intelligence and Big Data • Introduction to Computational Finance and Financial Econometrics (University of Washington) • Mathematical Methods for Qualitative Finance course ending (University of Washington) • Financial Engineering and Risk Management (Columbia University) • Algorithms: Design and Analysis (Stanford University) • Probabilistic Graphical Models (Stanford University) • Model Thinking (University of Michigan) • Statistics One (Princeton University) • The Caltech-JPL Summer School on Big Data Analytics (Caltech) • Quality Assurance (Lorton, Virginia) • Game Theory (Stanford University) • Python Analytics (APS) |
| Marketing Experience |
|---|
| • Drove profitable investment strategies by developing predictive model for Customer acquisition and marketing. • Ideated and developed predictive model for Customer journey to a purchase using Markov chains, Reinforcement Learning. • Paved the way to profitable marketing across product categories by creating product recommendation model (clickstream). • Ideated the methodology of the Cohesive Real-Time integration MRO omni-channel platform using combination of multiple identifiers across devices and touchpoints with data points collected alone the way for smart marketing and recommendations that drive sales and boosts ROI. • Behavioral targeting modeling. |
Language Skills
| Language | Proficiency |
|---|---|
| English | Fluent |
| Russian | Fluent |
Fields of Expertise
data science, machine learning, artificial intelligence, Campaign Marketing, advertising, Big Data Analytics, client analytics, predictive modeling, natural language text processing, Memory development, Ad-tech, applied statistics, condensed-matter physics, applied mathematics, leadership, data analysis