- Data Scientist with over seven years of experience in various aspects of data modeling, statistical analysis, and Machine Learning
- Profiicient in various python library include but not limited to NumPy, Pandas, seaborn, matplotlib, Plotly and sciPy
- Experience with various Machine Learning Algorithms include Linear Regresson, Random Forest, Decision tree
- Proficient in Base SAS, SAS Macro, SAS/Access, SAS/STAT and SAS/SQL.
- Solid experience in producing reports and performing data validation by employing ODS system as well as various SAS procedures, including, but not limited to, Proc Report, Proc Print, Proc Freq and Data Step
- Involved in extracting data from various sources including Oracle and MS Excel, preparing derived datasets for analysis and generating reports, tables, listings, summaries and graphs according to the Statistical Analysis Plan (SAP), Standard Operating Procedures (SOPs) and departmental guidelines
- Modified existing SAS programs and created new programs using SAS macro programs to ensure ease of use, increase the speed of modification and produce consistency in results strong problem solving and communication skills strong skills with Microsoft Excel as well as SAS and SQL
- Experience in documenting procedures, creating informative status reports and timelines for strategic resolution of problems
- Highly organized with attention to detail and excellent follow - through abilities
- Demonstrated rapid learning abilities and able to prioritize and multi-task
- Good team player but able to exercise individual judgment within standard operating procedures.
Confidential, Bolingbrook, Illinois
- Create machine learning tools that computed a predictive truck price with 90% of accuracy
- Working on the various projects with the Company’s Logistics Group in designing and implementing mathematical and predictive models to address business problems and simplify decision making process. Utilizing the agile/scrum methodology for all the projects. Rating Engine - Built a machine learning model using Random Forest Regression or gradient boosting Algorithm to predict the cost of future shipments using the historical truck load data.
- Currently Involved in the development and continuous improvement of the predictive model with the goal of giving highly competitive rates to the customers with optimized margin for the company.
- Developed intricate algorithms based on deep-dive statistical analysis and predictive data modeling that used to deepen relationships, strengthen longevity and personalize interactions with customers.
- Testing various analytics solutions using statistical modeling tools like R, SQL and Python.
- Utilizing Tableau for data visualization to ensure that data analysis is easily consumable by the business and clearly conveys insights to colleagues within the Analytics group.
- Actively monitoring and evaluating the impact and effectiveness of predictive model in production by generating reports using IRKernel package in R via Jupyter Notebooks. Carrier Assignment Model
- Load Planning Optimization Model - Building a Mathematical Model to create optimized load Plans both in the deterministic and stochastic networks across business units, locations and customers using R and Python (scikit-Learn, tensorflow) gathering business requirements by shadowing and interviewing national load planning team, Analyzing the results and efficiency of currently used optimization tool.
- Worked closely with the Company’s Logistics Group for designing and implementing processes and layouts for data sets used for modelling, data mining and research.
- Analyzed and processed complex data sets using advanced querying, visualization and analytics tools.
- Identified, measured and recommended improvement strategies for Key Performance Indicator across all business areas.
- Developed SSIS package and configuration for data extraction, transformation and load to SQL database and hand on experience in Azure development.
- Conduct multivariate analyses including but not limited to logistic regression, survival analysis, multilevel modeling, longitudinal analysis, sensitivity and specificity analysis, ROC analysis and modeling using SAS/Graph and SAS/Access SAS Enterprise Guide, SAS Forecasting for Desktop, SAS/ETS and SAS/SAT
- Programming in SAS and SQL to extract data from Oracle tables and create comprehensive datasets
- Merging and managing large datasets to create analytic datasets
- Produced tables, listings and figures for safety and efficacy analysis
- Monitored internal project timelines to ensure that deadlines for deliverables were met
- Reviewed programming outputs to ensure compliance with the Statistical Analysis Plan and study requirements
- Documentation of more complicated programs
- Generate descriptive and bivariate statistical tables and reports
- Understanding of health care data, both in general and specifically for TQIP/NTDB
- Working with other divisions of NTDB to complete specific statistical table requests
- Wrote and debugged SAS programs to generate tables, listings and graphs in support of Trauma Quality Improvement Program
- Analyzing and Modeling biological data using mathematical methods(Bioinformatics)
- Performed regular backups to ensure data preservation.