Sales Data Analyst

Nivedita Mangal

Tampa, FL +1-813-***-**** *******@****.***.*** LinkedIn.com/in/nivedita-mangal github.com/mangaln EDUCATION

University of South Florida MS Business Analytics and Information Systems (GPA: 3.66) Dec 2020 Rajiv Gandhi Technical University, India BE Information Technology (GPA: 3.70) May 2015 TECHNICAL SKILLS

Core Skills: C#, C++, R (zoo, car, dpylr, psych, foreign, tidyr), Python, SQL, PySpark, SAP Python Skills: pandas, numpy, matplotlib, seaborn, scikit-learn, pylab, scipy, keras, tensorflow, dash Machine Learning: Regression, Classification (KNN, Decision Tree, Logistic Regression, SVM, LSTM), Clustering Tools: R-Studio, Jupyter Notebook, Tableau, Power-BI, SAP Analytic Cloud, Eclipse (CDS), Excel, MS Access EXPERIENCE

Baillie Lumber Co. Data Analyst Intern (Supply Chain Industry, Hamburg, NY) May 2020-Present

• Created business-specific reports to analyzing real-time sales vs manufacturing data. Built KPI for the sales team.

• Created BI dashboards on SAC for the sales team, which help them analyze the best-selling products and factors affecting the sales which help them increase the overall sales by 10%.

• Build a linear regression model using Python, to predict the sales and gross margin of the materials for each plant.

• Lead a team of 6 members to improve the process of system synchronization by analyzing and maintaining each transport request and increase the efficiency by 20%.

• Performed ETL by creating CDS Views in SAP HANA using SQL queries for sales matrix reports which help the team to analyze the prime customers and increase the product demand. Automatic Data Processing Senior Analyst (Management Services Industry, Hyderabad, India) Jun 2015- Jul 2019

• Implemented complex client requirements in SAP and worked on multiple clients from APAC and EMEA regions.

• Conducted workshops for clients from different industries i.e. healthcare, finance, consumer goods, to automate the data integration for reducing human efforts by 30%.

• Created ETL data pipelines and data models using Informatica, by fetching data from client database using SQL queries and performed exploratory analysis on it using pandas, numpy, and matplotlib libraries in Python.

• Worked with cross-functional teams, solved complex problems, and developed new concepts.

• Created BI dashboards using Tableau to demonstrate the work progress and profits which helped the stakeholders to gain deep insights into the business and run it more efficiently. ACADEMICS

Project

1. Classification Model to Detect Clinical Depression from Speech data

• Cleaned and transformed the dataset using numpy and pandas packages in Python.

• Implemented RNN to predict the depression, using scikit-learn in python and achieved an accuracy of about 70%.

• Then implemented LSTM model, using keras and achieved an accuracy of about 85%. 2. Regression Model to predict Healthcare Quality & Expense of OECD countries

• Collected data from OECD and WHO website and feature engineered it using dplyr, devtools, tidyr libraries in R.

• Check the correlation and distributions of the variables and run the various models like OLS, panel data model with fixed and random effect, to see the yearly change in quality and expense of all the OECD countries.

• Compare the models using hausman test and find the best fit model. 3. Data Visualization Project

• Performed data cleaning, transforming, and merging on online product sales data from Kaggle using Python.

• Created dashboard on tableau to perform exploratory analysis on how and why the sales increases in recent days.

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