ARVIND RAM KARTHIKEYAN

716-***-**** adkaez@r.postjobfree.com https://www.linkedin.com/in/arvind-ram/ Buffalo, NY EDUCATION

The State University of New York (SUNY) at Buffalo Master of Science in Industrial Engineering 3.83/4 Dec 2020 Relevant Courses: Manufacturing Data Analytics, Programming for Analytics, Transportation Analytics, Design of Experiments, Six Sigma, Quality Assurance, Facilities Design, Logistics Management, Production Planning and Control, Work Physiology Anna University, Chennai, India Bachelor of Engineering in Mechanical Engineering 7.86/10 May 2018 SKILLS

Data Science tools: Python (NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn), Spark, R (Tidyverse), AWS Sagemaker Data Visualization: Tableau, Power BI, QlikView Excel: Power Pivot, VLOOKUP, VBA Cloud/DB: AWS, MySQL Machine Learning: Linear/Logistic Regression, Bagging, Boosting, SVM, Time Series, Cluster Analysis, PCA, K-nn, K-Means Technical Skills: Data Wrangling, A/B testing, Data Visualization, Data Analysis, RDBMS, Google BigQuery, Data Mining, ETL process, Hypothesis Testing, API, Data Warehousing, Forecasting, Marketing Analytics, Sentiment Analysis, Multivariate Analysis, ANOVA Certifications: Tableau Data Scientist, SSRS, IBM Data Science, SQL for Data Science, Six Sigma Green Belt, Marketing Analytics PROFESSIONAL EXPERIENCE

Data Analytics Project Intern Applied Medical Coatings LLC, Buffalo, NY Sep 2019 – Dec 2019

• Performed Exploratory Analysis by querying data from Relational Databases using SQL to address process challenges.

• Analysed various factors influencing coated material thickness on Surgical Blade, and identified the factors that caused variation using Root Cause Analysis, and collected the data from different operations using Design of Experiments.

• Developed a Linear model to predict the coated material thickness, and found optimal operational settings; saved $40000/mo. Data Analyst Maha Hydraulics Private Limited, India. May 2018 – Aug 2019

• Analysed e-commerce data by performing RFM, Pareto & Cluster analysis, and segmented customers to improve Target Marketing and enable alerts on those who are at risk of leaving and reduced the churn rate by 35% using Python.

• Leveraged time series models to forecast the demand and identified trends, seasonality, and key issues to slash OPEX by 15%.

• Automated the Root Cause Analysis Report by integrating SQL and Tableau and increased the efficiency of report generation by 12%.

• Carried out descriptive statistical data analysis through Excel with the help of R to give reports on various defects faced in machined components and suggested measures to overcome them, saved $0.5M

• Proposed Media Mix Strategy using Marketing Mix Model to achieve targeted advertising leading to increased revenue.

• Performed data cleansing and analysis of assembly line data, delivered Ad-hoc reports using Tableau and Excel. Tracked, analysed, and prepared presentations depicting key metrics & sales analysis using dashboard with data extracted from CRM

• Led cost reduction initiatives and brought down safety stock by 10%, saving WIP & storage cost by developing metrics for demand forecasting motor and power pack consumables requirement based on past projects. Industrial Engineering Intern Ashok Leyland Limited, India Jan 2018 – Apr 2018

• Led ABC & XYZ analysis and created an Inventory Management Dashboard to track the Inventory levels and various KPI (Key Performance Indicators) using Power BI; minimized the project schedule delays by 20%.

• Optimized Storage Zone by using K-means Clustering to reduce pick processing time in Warehouse using VLOOKUP & VBA macros. ACADEMIC PROJECTS

Predicting Hourly Citi Bike-Sharing Checkouts per Station - R, Python, Tableau

• Predicted the checkout count at any instance of time, thereby, determining the impact of weather on checkout count.

• Implemented feature engineering & data extraction techniques to add more variables and improved the model accuracy.

• Improved Gradient Boosting accuracy from 60% to 79% by performing parameter tuning using Gridsearch in scikit-learn. Analysing the Sales forecast methods for TATA Motors’ LCV – R, Excel

• Determined the appropriate forecasting method by comparing the metrics of different forecasting models for LCV sales.

• Estimated an increase in sales by 10% and aided S&OP by developing "what-If" scenarios and quantifying its impact.

• SARIMA model rendered the least error of 8.2% (MAPE), followed by Holt-Winter’s method with 13%. NYC Yellow-Taxi Demand Prediction - R, Tableau

• Predicted the hourly demand of NYC Yellow cab by considering various atmospheric conditions, retrieved using API.

• Performed EDA and found the significance of weather on number of rides and changes in demand pattern.

• XG Boosting Method provided the best results with a test error of 39.08 after performing randomized cross-validation. Data Retrieval from Employee Database – MySQL

• Constructed complex queries involving self joins, correlated subqueries, and recursive CTE, views for business requirements.