SUMMARY

• Over 5 years of experience as a Professional Qualified Data Analyst with extensive knowledge of Data Science and Analytics including Machine Learning, Data Mining, and Statistical Analysis.
• Involved in all phases of the SDLC (Software Development Life Cycle) from analysis, design, development, testing, implementation, and maintenance with timely delivery against aggressive deadlines.
• Extensive experience in Machine Learning solutions to various business problems and generating data visualizations using Python.
• Used Pandas, NumPy, Scikit - learn in Python for developing various machine learning models.
• Experience in designing visualizations using Tableau software and publishing dashboards, Storyline on web and desktop platforms.
• Good understanding of front-end technologies, such as JavaScript, HTML5, and CSS3.
• Worked with several Data migration projects, which led the application to run faster and better.
• Design, implement, or operate comprehensive data warehouse systems to balance optimization of data
• Access with batch loading and resource utilization factors, according to customer requirements.
• An excellent skill set for automating most of the mundane tasks using various scripting languages.
• Worked with number of reporting teams and developed a metadata models for automated reporting dashboards.
• Good understanding of requirements and goals. Involved in requirement gathering and validating requirements.
• Designed summary tables to retrieve the KPIs and other metrics in a very fast manner.
• Mined and analyzed huge datasets using Python and R languages. Created an automated data cleansing module using supervised learning model in python with Machine learning.
• Studied most of the machine learning concepts and used in real time projects with authorization.
• Prepared reports for internal and external audiences using business analytic tools.
• Resolve semantic discrepancies in data definitions that arise among multiple sources and projects.
• Experience with Data flow diagrams, Data dictionary, Database normalization theory techniques, Entity relation modeling and design techniques.
• Design and implement warehouse database structure. Also created and implement metadata processes and frame works.

PROFESSIONAL EXPERIENCE

Confidential

Data Analyst

Responsibilities:

• Responsible for developing data pipelines and integrations across different platforms like snowflake, Tableau server, slack, AWS and Databricks.
• Hosted site using GitHub pages which has several Tableau Server Dashboards embedded to view status of each control on a daily basis.
• Used Pandas, numpy, psycopg2, oracle Cx, snowflake SQL Alchemy modules in python for performing complex data transformations and ETL/ELT processes.
• Monitored and resolved issues of data flow on daily basis.
• Worked with data producers to resolve data discrepancies and logical data corrections which are occurring throughout reports.
• Implemented automated report distribution program for the daily routine tasks of generating reports and delivery using Databricks.
• Worked with Enterprise Service Risk team and developed solutions to move from manual sample testing to full population testing.
• Automated over 35 use cases using Python/Alteryx and saved testing team nearly 120 annual hours spent on manual validation.
• Developed automated processes for the reconciliation of user creation, access, and, termination data across multiple databases.
• Built Tableau reports/dashboards for management team and provided them with visuals of the validated data for each use case.
• Created and ran python/spark scripts on Databricks for complex controls that require extraction of large data sets.
• Created ad-hoc queries to extract data using SQL developer, Snowflake, PostgreSQL, MS SQL depending on the control.
• Led cross functional team and collaborated with SOX and NON-SOX teams to understand business and technical requirements and build time-saving approach to automate process.

Confidential

Data Scientist

Responsibilities:

• Responsible for design and development of advanced R/Python programs to prepare to transform and harmonize data sets in preparation for modeling.
• Used Pandas, NumPy, Scikit-learn in Python for developing various machine learning models such Random forest.
• Deep knowledge of a scripting and statistical programming language python.
• Advanced SQL ability to efficiently work with very large datasets. Ability to deal with non-standard machine learning datasets.
• Built forecasting models in Python using Gradient boost Regression Trees. Forecasted the revenue and the guest count for 21 days.
• Worked with K-Means, K-Means++ clustering and Hierarchical clustering algorithm to do segmentation of restaurants.
• Collected various store attributes and added them into our segmentation model in order to better classify different segments using clustering algorithms.
• Worked with outlier analysis with various methods like Z-Score value analysis, Liner regression, Dbscan (Density Based Spatial Clustering of Applications with Noise) and Isolation forest.
• Used cross-validation to test the models with different batches of data to optimize the models and prevent overfitting.
• For handling large transformations of data, used PySpark in Azure platform.
• Analyzed the SQL scripts and designed the solution to implement using Pyspark and developed scripts as per the requirement.
• Worked with tableau tool in order to represent the data in visual format and better describe the problem with solutions.

Confidential

Data Analyst

Responsibilities:

• Designed, Developed and Deployed application with team in timely manner.
• Created Unix Scripts for receiving and extracting the data files which is been sent by various market places around the world.
• Designed and implemented business rules from the scratch by a number of discussions with client in daily manner.
• Adhered to design standards and client guidelines.
• Designed a data flow model for each market, starting from external tables to the very end of summary tables.
• Collaborating with Product Managers and Software Engineers to provide guidelines on solid application design.
• Designed Postgre SQL modules for each market to validate and transform data while loading it into the database.
• Worked with pg Admin administrator to design storage allocation and load balancing.
• Created and implemented daily running jobs based on the timelines with a parallel partner Cognizant.
• Tuned every data retrieval process for better performance.
• Proposed some design ideas to eliminate redundant problems like cyclic dependency, campaign dependency and some data flow errors.
• Created an interface for the reporting team to access the data from the warehouse to create business reports for the users.
• Worked with reporting team to automate some of the business reports for the higher management review.
• Trained support team on every data flow error, logical discrepancies and reporting data issues.
• Created and implemented some of the new complex market places to the application as standalone resource.
• Tuned most complex markets to gain 28% of performance boost on selected markets.
• Re-designed some of the markets to cut down the critical issues by 83%. Received a best performer award for this particular achievement.

Confidential

Data Analyst

Responsibilities:

• Involved in Data mapping specifications to create and execute detailed system test plans. The data mapping specifies what data will be extracted from an internal data warehouse, transformed and sent to an external entity.
• Worked closely with stakeholders to understand, define, document business questions needed.
• Review system/application requirements (functional specifications), test results and metrics for quality and completeness.
• Designed and Developed Oracle PL/SQL Procedures and UNIX Shell Scripts for Data Import/Export and Data Conversions.
• Analyzed the source data coming from different sources (Teradata, SQL Server, Oracle and also from flat files like Access and Excel) and working with business users and developers to develop the Model.
• Used Informatica Data Quality as ETL tool to transform the data from various sources and bring them into one common format and load them into target database for the analysis purpose from Data Warehouse.
• Executed SQL queries to validate actual test results and match expected results as per financial rules.
• Responsible for maintaining the integrity of the SQL database and reporting any issues to the database architect.
• Design and model the reporting data warehouse considering current and future reporting requirement
• Involved in the daily maintenance of the database that involved monitoring the daily run of the scripts as well as troubleshooting in the event of any errors in the entire process.