Senior Business Analyst
NEC Corporation
مجموع سنوات الخبرة :6 years, 8 أشهر
• Analysing the Indian logistics industry, with a particular emphasis on ports, roads, and railroads, in collaboration with the Government of India, Indian logistics department, and NSIDC Logistics Data
Bank (NLDS), formerly known as DMICDC.
• Analysing the port operations of 17 largest Indian ports, which are essential to processing a large amount of the countrys cargo and include Jawaharlal Nehru Port (Nhava Sheva), Mundra Port, Chennai Port, and Kolkata Port.
• Examining the efectiveness of Indias port terminals, motorways, toll plazas, road network, trafic control, and industrial corridors like the Delhi Mumbai Industrial Corridor (DMIC).
• Examining the operational efectiveness, capacity limitations, and delays of Indian Railways freight transportation system, including its Dedicated Freight Corridors (DFCs), such as the Eastern and Western DFCs.
• Examining Container Freight Stations (CFSs) and Inland Container Depots (ICDs), which manage all import and export customs procedures in a multimodal transport logistics system.
• Accelerating the eforts of clients to evolve their data analytics strategy for real business impact through ensuring that it spans range of technologies, from dedicated analytics platforms and databases to real-time predictive business intelligence (BI) and data visualization tools.
• Perform navigating complexity of modern data and advanced predictive analytics, data visualization, data science and business intelligence, and put data to work to streamline operations and supply chains, improving the analysis into concise actionable conclusions to influence others to vision.
• Working closely with data engineers, and operation team to extract, transform and load data from multiple data sources, exploratory data analysis, exception identification, data modelling and feature engineering, using combination of manual processes, SQL queries, scripts, algorithms, and macros as necessary to fit the analytical requirements.
• Performing data analysis using Hive to retrieve the data.
• Performing univariate and multivariate analysis on various datasets to identify any underlying pattern in the data and associations between the variables.
• Performing data imputation using Scikit-learn package in python.
• Performing features engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn pre-processing.
• Extensively using python machine learning frameworks such as NumPy, SciPy, pandas, scikit-learn, seaborn to develop a variety of models and algorithms for analytic purposes.
• Developing and implementing predictive models using machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests, K-means clustering, KNN, PCA, and regularization for data analysis.
• Designing rich data visualizations to model data into human-readable form with Tableau.
• Designing, developing, and deploying models and analytics reports, Key Performance Indicators, scorecards, and dashboards.
• Preparing and presenting results of analysis and reports along with their relative impact(s) to the business stakeholders, all levels of management, and government ministries that helps expanding their facilities and upgrading infrastructure to handle rising trade volumes.
• Collected information from several sources, such as user interactions with the mobile apps, website, and landing pages.
• Monitored data including time spent on the platform, course completion rates, and user logins to assess user engagement and retention. determined the variables influencing user engagement and recommended ways to make improvements.
• Performed data analysis to find insights about user behaviour, course completion rates, and other pertinent metrics.
• Conducted analysis on assessing user consuming behaviors and discovered the value of customers with RMF analysis.
• Divided the user-base into groups according to their tastes, behaviours, and demographics and applied customer segmentation with clustering algorithms such as K-Means Clustering and Hierarchical Clustering.
• Built classification models include Logistic Regression, Decision Tree, Random Forest to predict Customer Churn Rate.
• Used F-Score, AUC/ROC, Confusion Matrix, MAE, RMSE to evaluate diferent Model performance.
• Built models to project course demand, student dropout rates, and other pertinent KPIs. employed machine learning techniques to enhance decision-making and forecasting.
• Maintained and created data dashboards and reports for executives, product managers, and teachers, among other important stakeholders. made sure the data shown in a way that is both comprehensible and useful.
• Prepared and presented results of analysis and reports along with their relative impact(s) to business and all levels of management.
• Identified opportunities for improvements and gaps. Reviewed accuracy of information and data integrity. Achieved performance improvement on average across client assignments.
• Performed various business and marketing analysis and assessment using Web analytics technologies (Google Analytics, AdWords, AdSense, SurveyMonkey, and Mailchimp).
• Performed data analysis to find patterns, trends, and insights to understand user behaviours and preferences, including customer interactions, network performance, and media consumption, usage patterns, network trafic, and content consumption.
• For better target services and marketing campaigns, segmented users into groups according to genre, content, demographics, and preferences and applied customer segmentation with clustering algorithms.
• Created word clouds and retrieved data from social networking platforms, worked on Text Analytics
• Utilized natural language processing (NLP) techniques to Optimized Customer Satisfaction.
• Designed and implemented recommender systems that utilized collaborative filtering techniques to recommend the content for diferent customers and deployed to AWS.
• Applied various machine learning algorithms and statistical modeling using Scikit-learn package in python.
• Designed, generated, reviewed, and published reports. Used aggregate and essential data from multiple sources to create complete analysis, improvement and/or recommendation(s).
• Prepared and presented reports to business stakeholders and all levels of management.
• Developed scenario planning tools/ models and sensitivity analysis. Prepared the forecasts and analyzed trends in general business conditions.
• Maintained and created data dashboards and reports for management and the product development teams, among other important stakeholders.
• In September 2019, received the Best Performer of the Month award.
• Worked independently and collaboratively throughout the complete analytics project lifecycle, including data extraction/ preparation, design, and implementation of scalable analysis and solutions, and documentation of results.
• Assured the quality of the data by removing errors, inconsistencies, and missing information through cleaning and preparation.
• Examined revenue-generating variables such as product, client categories, and geographical areas.
• Examined how pricing policies and marketing initiatives afected revenue and new client acquisition.
• Forecasted customer attrition (churn) and suggested loyalty and client retention tactics.
• Developed and maintained data dashboards and reports to provide the marketing and management teams the useful insights.
• Provided data-driven insights by collaborating closely with cross-functional teams in marketing, product development, and customer support.
• Oversaw and participated in the organizations OPEX collection plan for significant projects in Delhi, Noida, Gurgaon, and other northern states.
• Data for the organizations non-operational projects was acquired via internal ERP, historical records, and a variety of other manager and stakeholder communication channels.
• Completed a thorough inventory analysis and determined the non-functional assets.
• Developed machine learning algorithms for price prediction and market sentiment analysis.
• Provided python algorithms to minimize the frequency of trade loss of stocks in the portfolio.
• Applied various machine learning algorithms and statistical modeling like regression models, time series, statistical arbitrage, clustering, SVM to identify volume using Scikit-learn package in python.
• Worked on diferent data formats such as JSON, XML and performed machine learning algorithms in Python.
• Performed statistical analysis, sentiment analysis, and prediction analysis.
• Predicted future prices of the stocks with time trend, seasonality, cyclical and irregular fluctuations.
• Tracked performance indicators such as return on investment (ROI), drawdowns, ratios, and found opportunities when evaluating trading algorithms and strategies.
• Kept an eye on the models performance and took appropriate action when needed to make sure the risk parameters were followed.
• Found ways to improve the cost-efectiveness, execution, and trading infrastructure.
• Worked together to incorporate data-driven insights into trading operations with traders, risk
Subjects were SAPM (Security Analysis and Portfolio Management), Financial Services and Tax Analysis