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Master North Courses: Carolina In Operations Applied State University, Time Research Series; Raleigh, Applied NC Bayesian Analysis; Statistical Programming; Computer Simulation Techniques December (discrete 2018 event simulation, Monte Carlo); Stochastic Models; Logistics Engineering; Production Planning; Scheduling and Inventory Control Master Grenoble Bachelor Jawaharlal Skills In Graduate of International Nehru Technology Technological School Business – Of Specialization Business University (Singapore in Hyderabad, Computer Campus) India Science December May 2013 2015 Professional Certification
Certified Research Analyst (CRA), 2015 - certified by International Institute for Procurement and Market Research Technical Skills:
Programming Tools: Database: Tableau, Microsoft Languages: MATLAB, SQL Server, LINDO, Python, MySQL, Arena, PySpark, XLSTAT, Oracle R, SAS, StatCrunch, C, Java JAGS, MS Excel, MS PowerPoint, GanttProject, SAP, SalesForce Experience
Cypress HCM, San Diego, CA, Data Scientist June 2019 – Current
• Created analytic tools to aid data preprocessing in pyspark dataframe. Splitting, sampling by profile. Extracting keys from multiple nested json files. Created zeppelin notebook for demo of tools.
• Synthetic data generation, by understanding distributions and interrelations between fields. Learned Bayesian network and conditional probability distributions using pomegranate python package. Implemented with help of scipy.stats and numpy.random. Qorvo, Greensboro, NC, Data Science Analyst Intern May 2018 – December 2018
• Extracted data from different databases using SQL, applied data cleaning, transformation techniques. Handled outliers, null values, categorical data (one hot encoding) using pandas, and numpy packages.
• Applied regression, classification models, unsupervised and supervised machine learning algorithms with aid of SciPy, scikit-learn, XGBoost python packages to obtain insights from data. Visualized results using matplotlib, bokeh, and seaborn python packages.
• Clustered products based on lifecycle, implemented multiple models, and identified best performing model. Implemented using fastdtw, kshape, and scikit-learn hierarchical clustering python packages.
• Modelled prescriptive portfolio management applying Bayesian network; implemented using pgmpy python package. Laurus Labs, Hyderabad, India, Business Development Associate April 2015 – December 2016
• MIS reporting: Collected current sales stats and analyzed the quantitative data with information gathered from each account manager and reported the same to management team.
• Sales plan and Budget planning: Planned production for the sales plan and budget forecasted by account managers through coordination with various departments.
Laurus Labs, Hyderabad, India, Business Development Intern September 2014 – March 2015
• Performed analysis sales performance of all product lines and products across territories for each quarter using Tableau. Performed market research of competitor’s market share. Analyzed industry’s and competitor’s profitability and defining the contributors of profitability and deriving a strategy in line with the positioning and segmentation of the company’s market. Projects
Refueling Station Location (New Facility Location Allocation: MATLAB) The scope of the project is to estimate locations for new refueling stations for electric vehicles, by implementing a simpler MILP model in MATLAB. Primary data used for the project was traffic count data and location of existing electric fuel stations, which was obtained from NCDOT website. An estimate of Origin-Destination pairs was arrived at using a simple algorithm, for ease of computation.
Optimizing Customer Service Delivery for Lenovo Data Center (Markov Chains & Markov Decision Process: Tableau) The project sponsored by the Lenovo Data Center Group aims at optimizing their customer service delivery by analyzing historical data to improve future service deliver. The project included three key deliverables. Performed predictive analysis for predicting future service actions using regressions. Implemented Markov chains to understand the transitions among various service actions. Markov decision process was employed to arrive at an optimal policy for customer service delivery. Optimizing seating policy for customers at restaurant (Dynamic Programming: Python) The purpose of this research is to develop and test models for determining optimal seating policies to customers arriving in various party sizes at a restaurant. Implemented model by using value iteration. Arrival rates and service rates are obtained from research papers. Sensitivity analysis with various service rates, and resources was conducted.