SIMRAN BAWASKAR

Boston, Massachusetts ad2uus@r.postjobfree.com 857-***-**** LinkedIn Portfolio GitHub OBJECTIVE SUMMARY

I'm enthusiastic about securing a role that enables me to engage with data and extract business insights from it. EDUCATION

Master of Science Engineering Management – GPA 3.6 Dec 2023 Northeastern University, Boston, Massachusetts

Bachelor of Engineering in Mechanical Engineering Oct 2020 University of Mumbai, Mumbai, India

SKILLS

Management: Six Sigma, Lean, DMAIC, ABC Segmentation, Forecasting, Value Stream Mapping, Root Cause Analysis, Linear Programming, SCRUM, JIRA, CRM, KPI, Agile, Software’s & Tools: Advanced Excel (VLOOKUP, Pivot tables, Macros), Tableau, Power BI, Snowflake, DAX, Alteryx, Solidworks, Ansys, AutoCAD, QlikView, SharePoint, TMS, SAP, MySQL, SQL, SAP HANA, Python, SSIS (SQL server reporting services), SSRS (SQL server integration services), SSMS, salesforce Certificate: Scrum Master, Google Data Analytics

EXPERIENCE

Business Intelligence Analyst Co-op, Bendix Commercial Systems LLC. Dec 2022- Aug 2023

• Identified product trends and patterns in the data to identify issues and suggested improvements in process to operations.

• Generated KPI reports using power automation related to sales to provide insights for various stakeholder meetings.

• Resolved technical issues for managers, reporting team and clients related to dashboards and automation process.

• Mapped out existing business processes and lead requirements sessions with key stakeholders to document requirements.

• Created a database in Snowflake to store shipment data at Part Group level and extracted required data using SQL queries.

• Organized and merged data from multiple sources to create Master Database and performed ETL through Power Query.

• Extracted data from Snowflake into Power Bi to create reports for weekly invoicing at part level using Power Bi DAX.

• Analyzed the data in Power Bi by creating visualization dashboard and gave insights on data regarding Premium Shipments.

• Maintained and published reports for customers and managed any change requests related to the project.

• Supported strategic business projects and SCRUM agile business initiatives and log activities on JIRA. Data Analyst, V S Jadon & Co. Valuers LLP Dec 2020 - Aug 2021

• Supported finance and business team to develop analytics, conduct trent analysis and initiated inventory improvements.

• Automated process of reporting, post routing letter generation for new vendors creating triggers through Power Automate.

• Constructed a database on MS SQL Server by creating a schema for various types of data sets and did data mapping.

• Performed ETL by extracting data from SAP into Excel through SAP HANA and connected it to Tableau dashboard.

• Collaborated in different teams to improve process through SCRUM agile business initiatives and log activities on MIRO.

• Monitored KPIs for analyzing total freight spend to understand difference between forecasted and actual savings.

• Build training modules and trained team by working cross functionally with sales team and increased productivity.

• Managed business relationships using CRM (Customer Relationship management) and improve profitability by 10%

• Created SQL Scripts for ETL processes data cleansing, normalization, and transformation for operations analytics project. PROJECTS

Sales Analysis using Python, Northeastern University Sept 2023 - Dec 2023

• Analyzed an extensive dataset of sales of electronics using python to identify trends and relationships influencing sales.

• Performed data analysis and data cleaning Pandas, Matplotlib and NumPy libraries to understand patterns in dataset.

• Created interactive visualizations for sales distribution, performance, customer, and geographic analysis on Tableau. Car Price Prediction using ML, Northeastern University Aug 2023 - Sept 2023

• Cleaned dataset by addressing null values, ensuring data integrity, and utilizing one-hot encoding for categorical variables.

• Conducted correlation analysis using heat maps to identify influential factors among features and visualized distribution.

• Trained machine learning models including Decision Tree, Random Forest, Gradient Boosting and K Nearest Neighbor.

• Evaluated models using R-squared error and recommended Gradient Boosting for car price prediction with 89% accuracy.

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