Anton Laptiev, Phd Email & Phone Number

United States

San Francisco, Ca, Us

San Francisco, Ca, Us

Lead a DSML team implementing ML solutions across multiple orgs, including Risk, Operations & Member Experience, Dispute Operations

San Francisco, Ca, Us

Designed, implemented, and maintained real-time fraud prevention models, resulting in significant cost savings by preventing millions of dollars in fraud losses:- Card transaction fraud- P2P transfer fraud- Dispute fraud- Mobile check deposit fraud- Inbound ACH transfer fraudDeveloped and deployed a semi-supervised framework which utilized a teacher-student approach to incorporate unlabelled data into supervised model training

Purchase, Ny, Us

I was a part of R&D team focusing on product development. In particular, I prototyped, tested and productionized ML models to improve existing and create new solutions related to passive behavioral biometrics, fraud detection, device and user identity resolution. I leveraged the power of Python ML libraries such as XGBoost, LightGBM, Tensorflow, Keras, PyTorch along with AWS tools such as EC2, S3, Glue, Lambda, Sagemaker to produce highly scalable and easily deployable real-time predictive solutions integrated into Mastercard IoT and Cybersecurity products

Vancouver, British Columbia, Ca

At Intiveo I was responsible for enabling product managers and key stakeholders to efficiently track the success of company's marketing initiatives and product improvements, test and validate their hypotheses, optimally target sales and marketing campaigns, and determine the most promising directions for product innovation. - built the Analytics Data Warehouse (AWS RDS PostgreSQL) by leveraging the complex automated ETL pipeline (Python, Apache Airflow) combining data from the OLTP database, Salesforce and Zendesk APIs- performed cohort-level analysis of key SaaS KPIs using Python and PostgreSQL- created predictive models for LTV forecasting using Python StatsModels and Scikit-Learn- designed and prototyped an AI-based smart messaging system for automated selection of optimal message content, delivery type, delivery time and recurrence interval based on patient profile (patent pending)

Montreal, Qc, Ca

At Adaware I was a part of a data team responsible for development of an automated and highly scalable BI monitoring and reporting system. We ingested gigabytes of incoming production data, performed joins, aggregations and transformations and produced dashboards and reports for tracking important KPIs. In addition, I supported business decision making by creating predictive models for customer retention and LTV analysis. - built, tested and maintained Big Data Analytics Data Warehouse and automated ETL pipeline using S3, MongoDB, Apache Parquet, Spark (scala) and Hive - performed cohort-level analysis of key business KPIs using Spark SQL- created predictive models for customer LTV forecasting using Python and R- developed machine learning algorithms for malicious URL detection using Spark MLib and XGBoost (JVM)

Vancouver, British Columbia, Ca

- Created an automated data pipeline for extraction, transformation, linkage and integration of cross section and longitudinal data from the U.S. Census data repositories- Performed data quality control and quality assurance activities as related to data management and analysis- Assisted with development, collection and maintenance of comprehensive data documentation- Conducted exploratory data analysis, develop data mining, data imputation, and data matching algorithms- Performed data analysis and statistical modeling using programming tools and statistical software (Stata, Matlab)

Vancouver, British Columbia, Ca

Conducted lab and review sessions for several undergraduate-level Statistics and Econometrics courses covering the following topics:Basics of Probability, Discrete and Continuous Random Variables and their Distributions, Point and Interval Estimation, Hypothesis Testing, Regression Models, Time Series Analysis, Time-Varying Volatility and ARCH Models, Panel Data Models, Analysis of Discrete, Censored and Truncated DataDuties:- Conducted discussion and laboratory sections for groups of up to 35 students- Designed practical problems for discussion sessions involving analytical problem solving and computational exercises using statistical software- Developed programming scripts for data analysis and statistical modeling used for introducing students to the analysis of large datasets such as Canadian Census- Held regular office hours to provide personal academic advice to students- Evaluated student performance by marking problem sets and exams

Doctor Of Philosophy - Phd, Economics

Master’S Degree, Economics

Master'S Degree, Operations Research

Bachelor'S Degree, Management Information Systems