GOPI C UPRETI

Email: ady1m2@r.postjobfree.com Cell: 316-***-****

EDUCATION

Master of Science (MS): University of Hawaii, USA

EXPERIENCE SUMMARY

Mr. Gopi Upreti, Data Scientist, and a Certified Machine Learning Specialist with ManTech International Corporation, has wide range of experiences in all aspects of Data Science, Data Management and Machine Learning (supervised, unsupervised, deep learning) Algorithm development. Mr. Upreti has been involved in the development of anomaly detection and forecasting algorithm, analyzed high volume data, Mutual Funds, and financial transaction data, discovered data trends and patterns, and provided business intelligence solutions to various agencies of federal government, developed models and search tools, produced analysis products and summary reports.

Mr. Upreti’s functional expertise includes statistical data analysis, machine learning and the application of quantitative and qualitative modeling techniques (linear & logistic regression, decision trees, XGradient Boosting, Random Forest, Deep Neural Network, Cluster and Principal Components Analysis), data management, software development, and testing and his skill areas include: ETL, business intelligence solutions; systems analysis; system configuration, quantitative statistical analysis, data mining and modeling and model development.

Clearance: Active Top-Secret Clearance (TSC)

Certifications

AWS Certified Machine Learning – Specialty

Microsoft Certified Azure Data Scientist Associate

Microsoft Certified Azure Fundamentals

Certified SAS Base Programmer

Skills

Unsupervised Machine Learning methods including Cluster Analysis (e.g., K-means, K-nearest Neighbor, Hierarchical, Deep Belief Networks, Principal Component Analysis), Segmentation, etc.

Supervised Machine Learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc.

Experienced in full lifecycle of machine learning model development activities; including data extraction, feature engineering, feature development, validation and implementation/assessment

Proficient with relational databases, using SQL/PLSQL or similar to extract, transform and load data in support of EDA and model development.

Combined credit bureau, customer transaction and bank data, identified and prepared predictor variables, involved in the process of developing of Credit Risk Forecast Models/Credit Scoring Models and validated models to enhance the understanding of credit behavior of the borrowers.

Pattern recognition and extraction, automated classification, categorization, and entity resolution (e.g., record linking, named entity matching, deduplication/ disambiguation).

ETL, SAS BASE, SAS Enterprise Miner (EM), SAS Enterprise Guide (EG), Python Anaconda, Machine Learning, Deep learning (TensorFlow), GGPLOT,Tableau, SAS Visual Analytics, Keras, PyTorch and NLP

EMPLOYMENT HISTORY

EGlobalTech/Bureau of Labor Statistics (BLS)

SAS Developer September 2022 -- Date

Extract expenditure data, performed data manipulation tasks including data profiling and creating SAS datasets, perform statistical data analytics and modeling.

Gathering and analyze requirements, design, and develop SAS applications.

Perform data imputation and create SAS reports (Proc Report, Proc Tabulate, Proc freq. ODS System)

Run SAS macros to produce monthly, quarterly, and yearly reports on consumer expenditure data

PNC Bank, PA

Data Scientist / Modeler August 2021 – March 2022

• Created data tables (data ingestion, transformation & preparation) and loaded into ORACLE relational

database system using SAS and Python

• Run SQL queries on HIVE and IMPALA to extract summary data (counts, descriptive statistics) for validation

and analysis purpose.

• Developed and implemented Time Series Forecasting model using SAS for volume forecasting of alerts

of potential anomalous AML behavior.

• Performed data analytics, feature engineering, implemented machine learning algorithms (logistic regression, random forest, gradient boosting, decision tree etc.) in the development of targeting models for suspicious and anomalous suspicious AML behavior.

ManTech International Corporation, Herndon, Virginia

Data Scientist and Simulation Specialist December 2017 to July 2021

Ingested data from various sources, extracted, transformed, and loaded (ETL) data tables onto the relational data base system and cloud storage system using SAS and Python

Performed data analysis and Ground Truthing (GT) validation task on departed passenger flights and system performance evaluation.

Prepared data (data cleansing, normalization, and standardization etc) for machine learning and model development using SAS and Python.

Used SAS, Python for Data Science work such as Data Extraction, Data Cleaning and Preprocessing

Used Python for Algorithm testing and Prototyping.

Performed data analytics and feature engineering, developed, and implemented machine learning algorithms in the development of targeting models for suspicious and anomalous behavior.

ELDER Research – Predictive Analytics Consulting Arlington, VA

Senior SAS Statistical Data Analyst January 2017---November 2017

Extracted Mutual Funds Holding data from Oracle database, Mutual Funds performance and monthly historical returns data from Morningstar Application Processing Interface (API), performed data cleansing, implemented ETL process and prepared analysis table for reporting.

Performed statistical analysis including regression analysis to generate required statistics (Alpha, Beta, Standard Deviation, R-Square, Chi-square, Residual Standard Deviations, Information Ratio, Bias Ratio, Sharpe Ratio, Jensen’s Alpha) and other performance and predicted statistics.

Computed Modern Portfolio Theory Statistics (MPT statistics) based on the Capital Asset Pricing Model (CAPM) by running Ordinary Least Squares Regression (OLS) of the fund’s excess return over a risk-free rate compared with the excess returns of the selected benchmark indexes

Deployed the statistical master base table in the stored process server to be used in the reporting, produced Mutual Funds Performance Summary Statistical and Visual Graphical Reports.

Booz Allen Hamilton- Washington, DC

Client: Office of the Assistant Secretary of the Navy (Financial Management & Comptroller) Office of Financial Operations.

SAS Subject Matter Expert (SME) August 2013 to 2016

Served as a SAS subject matter experts (SME) for Navy’s financial management office (FMO) facilitating the migration of Navy’s Transaction Universe data from third party database to Navy owned database system.

Extracted data from various sources and feeder systems, created SAS datasets and performed reconciliation of Navy’s audit readiness financial transaction universe data

Performed data quality management tasks including data formatting, conversions, mapping, data cleansing and profiling, descriptive statistics and quantitative drill down

Involved in collaborative unsupervised machine learning effort in developing Multivariate Analysis of Stealth Quantities (MASQ) Algorithm for Financial Audit Readiness data with BAH data scientist team in Norfolk. The MASQ algorithm identifies the outlier/anomalous transactional data that would assist analysts in identifying potential problematic audit area.

Provided training and technical support to end users, conducted database migration validation test in the test and production environment, complied test results and produced reports

Techanatomy, Fairfax, Virginia

Senior SAS Analyst/Programmer October 2012 - July 2013

Client: Financial Crime Enforcement Network (FinCEN), Treasury Department

ETL/Data Management/Analytics

Extracted BSA data, bank wire transfer data, performed data cleansing, data profiling, normalization, standardization and created SAS datasets.

Analyzed financial transaction data, discovered data trends and patterns against fraudulent financial transactions within the banking industry with a particular focus on identifying money laundering activities.

Performed fast queries and developed reusable SAS code to support decisions by senior FinCEN Analysts, developed search tools, produced quick-turnaround analysis products, data table, excel data sets, summary tables and reports.

Skills: ETL, Financial Transaction Data, Anti Money Laundering Research Techniques, SAS BASE, SAS Enterprise Guide (EG), Data Quality, Search Tools, Data Mining. SAS Fraud Framework Analytics

ManTech International Corporation, Fairfax, Virginia

Client: Department of Defense (TRICARE - Military Health Care System, MHS), Fairfax, Virginia

Senior SAS Programmer January 2012 – September 2012

ETL/Data Management/Analytics

Extracted Customer Health Care Survey data from vendor files, created SAS datasets, performed statistical analysis, produced tables with descriptive statistics, extracted health care claim data from M2 and BOXI database, created data tables for review, analysis, and evaluation.

Developed master data dictionary, validated vendor survey data, and produced validation and summary reports. Designed ETL process, loaded TRICARE health SAS datasets into the Military Health Data Repository (MDR).

Developed, reviewed, analyzed and documented SAS codes into baseline functional requirements in compliance with Military Health Data Repository (MDR) standard

Skills: SAS ETL, UNIX and Window Platforms, Data Validation, Survey Research, Warehousing, Health Care Data Management, Statistical Data Analysis, Descriptive Statistics.

Client: Fannie Mae Herndon & Washington (DC) office (Wisconsin Ave).

SAS Programmer /Business Analyst August 2010 – December 2011

ETL/Data Management

Extracted data from flat files (Equifax), created SAS data files, wrote and tested SAS script against the data, created tables in production environment.

Loaded SAS CSV data file into ORACLE database tables, transferred data to the test environment, conducted population validation tests (Oracle Database) in pre-production and acceptance environments using SQL Scripts, compiled, organized, documented and stored the test results and related documents in the SharePoint for organizational uses.

Manipulated data and performed data cleansing and data integrity check.

Credit Risk Modeling/Predictive Analytics

Performed descriptive statistical analysis and produced report with descriptive statistics, tables and charts.

Combined credit bureau, customer transaction and bank data, identified and prepared predictor variables, involved in the process of developing of Credit Risk Forecast Models/Credit Scoring Models and validated models to enhance the understanding of credit behavior of the borrowers.

Calculated the Probability of Default (PD) using financial, bank and transaction data using SAS Enterprise Guide (EG) and SAS Enterprise Miner (EM).

Digital Systems LLC, Arlington, Virginia

Statistical Data Analyst/SAS Programmer July 2008 – July 2010

Data Management/Analytics

Manipulated and managed data in SAS, SPSS, performed advanced statistical analysis solutions (univariate and multivariate analysis of variance, cluster and path analysis, principle component and factor analysis, analysis of covariance, survival & longitudinal analysis, logistic and linear regression modeling)

Developed and implemented logistic regression modeling for direct mail insurance by targeting responsive customers and minimizing risk.

Involved in the development of a net present value (NPV) model that was used to optimize the selection of prospects using SAS BASE, SAS/SQL, SAS Enterprise Guide and SAS/Enterprise Miner.

Defined the process to build predictive model, objective functions, variable selection and preparation and the statistical methodology and model validation.

Implemented model validation and developed diagnostic tables and graphs that demonstrated how model can be used to improve the efficiency of the selection process for a life insurance acquisition campaign.

Education, Skills, and Certificates

Skills/Job Highlights:

Strong background in Data Science, Data Management, and Machine Learning/Algorithm Development

Expertise in Software Development, Systems Analysis, System Configuration, Data Mining/Modeling, and Quantitative Statistical Analysis

Technical Skills:

Statistical data analysis (univariate, multivariate), Linear and Logistic Regression, Decision Trees, XGradient Boosting, Random Forest, Deep Neural Network, Cluster and Principal Components Analysis, ETL, SAS BASE, SAS Enterprise Miner (EM), SAS Enterprise Guide (EG), Python Anaconda, Machine Learning, Deep learning (TensorFlow), GGPLOT, Tableau, SAS Visual Analytics, SQL/PLSQL, Keras, PyTorch and NLP

Education

M.S. (Science), University of Hawaii at Manoa, Honolulu, Hawaii, USA

M.S. – Envt. Resource Management

University of Hawaii at Manoa, Honolulu, Hawaii, USA

Certifications:

AWS Certified Machine Learning Specialty

Microsoft Certified Azure Data Scientist Associate

Microsoft Certified Azure Fundamentals

Certified SAS Base Programmer

Key Demographics:

Clearance – Top Secret

Location preferences – Northern VA/DC

Contact information: 316-***-****; ady1m2@r.postjobfree.com

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