Engineer Data Analyst

United States of America 1-312-***-**** **********************@*******.***

ABOUT ME

Focused, quick-learning and meticulous strategic analyst with five years of IT experience, seeking an opportunity to upgrade my technical, collaborative skills and analytical ability. EDUCATION

Illinois Institute of Technology, Chicago, IL Jan 2019 –May 2020 Master of Data Analytics and Management (Information Technology Management) - GPA 3.875/4 Thiagarajar College of Engineering, Madurai, India Aug 2009 - May 2013 Bachelor of Technology in Information Technology - GPA 8.91/10 CORE SKILLS

Key Skills: Business Analysis, Data Analysis, SDLC, Agile, Data Mining, Data warehouse, Machine Learning Tools: Tableau, SPLUNK, Jupyter notebooks, Power BI, WEKA, Pentaho, JIRA, SOAPUI, MS Office, Github Programming Skills: R, Python, SQL, MYSQL, JAVA, XML, JSON PROFESSIONAL EXPERIENCE

British Airways – TATA Consultancy Service

Business Analyst October 2015 — Dec 2018

New Distribution Capability (NDC)

• Redesigned existing ba.com web services to reduce the number of dependent services and increase efficiency.

• Shouldered the responsibility of bridging the gap between the client and technical team for the business and domain understanding with ability to review, consolidate, and reverse-engineer complex technical and systems documentation.

• Lead discussions and produce an assortment of deliverables: gap analysis, impact analysis, current state analysis, process analysis, requirements analysis and created formal technical documentation such as reports, training material, layout and UML diagrams.

• Worked in an agile environment during sprint planning, participating in scrum meeting with various stakeholders, elicit requirement, user story development, backlog creation and prioritization.

• Analyzed and Understood the business needs, reviewed change requests submitted by Stakeholder and decomposed high-level requirements into functional, non-functional requirements to derive user stories and elaborate specifications through the documentation.

• Engineered test plans and scenarios appropriate to functionalities and validated test results to verify scenario coverage and to ensure zero defects during user acceptance testing.

• Conducted business user acceptance testing, Release Testing and validated the applications for more than 15 releases to confirm functionalities, possessing business and technical expertise in a wide array of customers & commercial oriented business solutions.

• Applied various complex SQL queries to access, test, update and substantiate data on the customer, agent and operation databases, holding millions of records.

• Structured Jira dashboards for task assigning, defect tracking and report generation which depreciated 70% of tracking efforts. Web Browser Log Data Analysis

• Continuously monitored customer behavior to assist in the identification of fallout during flight booking by developing Splunk SPL

(Search Processing Language) filters and Dashboard/Visualization.

• Based on data-driven insights, implemented periodic data caching to restrict hits to the repository, which reduced service fee by 14%. Data Analyst September 2013 — September 2015

Predictive Analytics

• Examined and pre-processed various airlines’ Customer Relationship Management records which include Data Cleaning, Normalization, transformation and condensed it into a coherent story.

• Oversaw frequent flyer’s prime sale, post-sale preferences under various circumstances using Regression and Classification Techniques.

• Depending on predictive and statistical modelling, implemented targeted advertising which increased the ancillary sales by 12%. Customer Trend Analysis

• Conducted Trend investigation to forecast demand by segmenting similar objects using K-means clustering and predicted the ticket, ancillary sales using Time-series (ARIMA) prediction model: visualized insights for senior management using Tableau.

• Discovered hidden patterns from unsupervised post-sale data using Apriori Association techniques. ACADEMIC PROJECTS

A study of sales through consumer behaviors on Black Friday Purchases January 2019 — April 2019

• Identified sales transactions made in a retail store, processed data for analysis and found correlations between various attributes before demonstrating regression, classification analysis using R and predicted purchase of users hinge on their historical data.

• Implemented machine learning algorithms and parameters that affect their performance using Python libraries like Pandas, Numpy, Scikit, SciPy, matplotlib.

Movielens Recommender System August 2019 — November 2019

• Build a recommender system to recommend movie based on users’ behavior.

• Implemented both Memory-based and Model-based Traditional recommender models in R using LibRec.

• Created K means clusters using Python libraries to predict top K movies and recommend for users. Sivaranjani Prabasankar https://www.linkedin.com/in/sivaranjani-prabasankar/

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