Data Analyst Python

Daron Sohi

Tempe, AZ · *****@***.*** · 480-***-**** · https://www.linkedin.com/in/daronsohi

EDUCATION

Arizona State University Tempe, AZ

Master’s in Industrial Engineering (Industrial Statistics), GPA: 3.9 Aug 2017 May 2019 National Institute of Technology, Punjab India

Bachelor’s in Mechanical Engineering, GPA: 3.75 July 2009 May 2013 TECHNICAL SKILLS

Database and Big Data Tools : MySQL,T-SQL, MS Access, Hadoop, Google Cloud, Salesforce, SSRS, SSIS, SharePoint, AWS Statistical and Programming Tools : Python, R, SAS, SPSS, Minitab, SAS, JMP, SQL, Visual Basic (VBA), MS Excel, Java, MS Office Business Intelligence Tools : Tableau, SAP, CRM, KANBAN, ERP, Workato, Power BI, MS-Project, Tableau Machine Learning (AI) : Decision Tree, Neural Networks, MLP, SVM, PCA, Random Forest, XGBoost, Data Science RELEVANT PROFESSIONAL EXPERIENCE (RELEVANT PROJECTS) Kovach Enclosure Systems LLC Tempe, AZ

Data Analyst Aug 2019 Present

Resources and Operations Optimization:

• Gathered business requirements and develop financial models for forecasts and predicting cash flow (revenue cycle) using financial data for project life cycle using operations research algorithm

• Automated several reports using SQL and Python for improving the data quality and reduce manipulation of Excel sheets

• Designed a Time-series predictive model to estimate the resources for workforce management and forecasted the capacities of our teams for project management and production support

• Performed financial analysis, extracted data, and made Power BI financial reports and dashboards for users and higher management for monitoring the expenses on each project against their respective cost codes and to provide business insights

• Created SQL scripts for daily extracts, ad hoc reporting, use case reporting and Python scripts for analyzing large data sets

• Working on controlling the document flow by developing a nomenclature and SOP’s for Engineering drawings and automating the extraction of files using Python and Workato for standardization and improve business performance Godrej & Boyce Mfg. Co. Ltd. India

Data Analyst- Assistant Manager Sep 2013 April 2017 Customer Segmentation, Targeting, and Retention Model: Built a Random Forest-based predictive model on complex data sets to identify consumers with high churn out tendency, followed by collaborative filtering suggested cross-selling of other warranty plans in order to retain and improve business process

• Targeted around 10,000 small and medium-scale industries(B2B), the model was deployed after testing for 3 months.

• Performed data mining, data analysis, data integrity, data mapping, data processing, statistical analysis, and data modeling

• Developed necessary SQL queries (sub, embedded and nested queries) for testing, and maintaining high-quality data sets

• Writing and designing SQL queries for data extraction, and conversions (ETL) from multiple data sources and data warehouse

• 14% increase in retention rate within 1 year of model deployment Call Plan and Supply Chain Team Design:

Designed a Supply Chain Team from scratch with the optimized workload and leads for every representative (reps)

• For 23 geographical territories with an equal workload and leads, most suitable reps were identified using the operations research algorithms by using work experience, preferred location, rapport, scheduling, and 2 other features

• Investigate data, develop solutions, coding, and applying analytical skills on Tableau for continuous process improvement

• Produced clear financial and status reports to internal stakeholders for training and performed ad-hoc analysis to improve business strategy, operational performance, and customer experience

• 55% market reach was achieved in the first 6 months of Aerial Work platform launch Demand Forecasting model for associated dealers and vendors to optimize Supply Chain and Logistics Operations: An ARIMA/X and transfer function-based time series model on manufacturing data to predict monthly sales of consumables and other warranty products to manage inventory. Model is operational for the last 3 years and bottom line improved by 17%.

• 25% fewer product stock-outs reported and inventory reduced by 30% across stores within a year of model implementation

• Troubleshooting technical issues, analyze data, and implementing data analytics to analyze performance metrics and KPIs

• Effectively maintained documentation and collaborating with business stakeholders for data driven solution and presentation

• Wrote progress reports, presentations, and publishing BI dashboards using Tableau for data visualization and data reporting Anomaly Detection Project:

• Natural Language Processing machine learning-based model to identify fraudulent sales leads and warranty claims

• A basic text mining model to identify trends, claims and leads filed under wrong coverage

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