- Led teams of sizes 3 - 10 people (onsite and offshore) providing though leadership, modeling and mentorship to more than 10 projects.
- Built many customer and business centric dashboards to answer key client questions, monitor models, produce analysis results using Tableau and QuickSight.
- Conducted workshops on Docker, AWS Sagemaker, GPU processing; and weekly ML strategy review sessions to team members that include MS and PhD Data Scientists.
- Certified AWS developer having experience to architect and implement solutions in AWS solutions such as EC2, S3, Glacier, lambda, Athena, Glue, RDS, Redshift, Sagemaker, Aurora, EMR, Quicksight, etc.
- Built ML models such as forecasting model, risk models, classification, fault prediction, next product to buy, rule based models, sentiment analysis, propensity models, etc.
- Experience extracting data from Big Data bases such as MySQL, Hadoop, MariaDB, PrestoDB, Hive, Kafka, Elastic Search.
- Worked closely with business, data governance, SMEs and vendors to define data requirements.
- Built models using LSTM, Neural Nets with keras on Tensor Flow, MinMax, HMM, logistic regressions, Random Forests, SVM, k-NN, time series models using packages such as ggplot, dplyr, numpy, sci-kit learn, pandas, matplotlib, etc.
- Automated by building workflows to extract data from various REST APIs and databases, processing responses, data transformations in python and R.
- Reported analytical findings to C-level executives using dashboards built in Splunk, Tableau, Qlik View, R-Shiny.
- Managed teams to perform data analysis on classification and forecast models, statistical models, risk analysis and solved data driven problems using SPSS, SAS E-Miner, R, SAS, Python, E-Views, Tableau, Qlik.
TOOLS: & DATABASES: RStudio, python, Spark, AWS, SPSS, SAS, Hadoop, Hive, MongoDB, Cassendra, Zeppelin, S3, Aurora, Glacier, Elastic Search, EC2, Lambda, Quick Sight, Splunk, Tableau, Qlik, Adobe Site Catalyst, Google Analytics, MS Visual Studio, Excel, MS PowerPoint.
Lead Data Science
- Manage ad-hoc analytical requests from Flights, hotels, cars stake holders.
- Implemented end-to-end CICD enabled platform using AWS Sagemaker to deploy models to production at scale.
- Add ML model improvements and new feature additions to Krazyglue (LOB recommendation engine of Confidential ) and perform ML model improvements using deep learning techniques.
- Collaborate with UI and engineering team for new ML feature roll-outs and orchestrate AB testing to report and track performance.
- Find answers to key business questions by building complex queries in Hive on Hadoop, Teradata.
- Analyze website traffic data to Brand Confidential, Hotels, Hotwire, Travelvelocity, Orbitz, trivago websites and generate dashboards in Adobe Analytics.
Tools: Python, pyspark, Hadoop, Teradata, HiveQL, Tableau, AWS EC2, SageMaker, Aurora, QuickSight, PowerBI.
Lead Data Scientist
- Extracted inventory flow and stock level data across various nodes (hubs, stores, etc) by joining tables from more than 10 databases.
- Assessed the scope and scale of project based on current and future scope of project.
- Implemented hybrid architecture using Hive, PrestoDB, MariaDB, AWS Aurora, S3, Glacier, Spark, EMR.
- Forecasted Average weekly demand using historical demand data and calculated safety stock, cycle stock and max stock across all nodes (Hubs, stores, etc) based on predictions.
- Built MinMax, LSTM, ARIMA machine learning & deep learning models and sent results back to local databases.
- Build Tableau dashboards for ad-hoc analysis and to compare results.
Tools: AWS Aurora, S3, Glacier, EMR, Python, PySpark, Rest API, Linux, Hive, PrestoDB, MariaDB, Tableau.