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Click here or scroll down to respond to this candidateCandidate's Name
DATA ANALYSTHudson, NJ PHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedInPROFESSIONAL SUMMARYOver 3 years of experience as a Data Analyst, skilled in data manipulation and visualization using tools such as NumPy, Pandas, Matplotlib, TensorFlow, and more. I am proficient in presenting data-driven insights through Tableau, Power BI, and advanced Excel. Additionally, I have experience with Agile (Scrum), cloud technologies like AWS and Microsoft Azure, and working with databases such as MySQL, MongoDB, and PostgreSQL. SKILLSMethodologies SDLC, Agile, WaterfallLanguage Python, SQL, RPackages Pandas, NumPy, Matplotlib, SciPy, Seaborn, Scikit-Learn Data VisualizationToolsTableau, Power BI, Advanced Excel, StatisticsDatabase MySQL, MongoDB, PostgreSQL, Teradata, Oracle, Hadoop IDEs Visual Studio Code, PyCharm, Jupyter Notebook Cloud Technologies Amazon Web Services (AWS)Other Technical Skills SSIS, SSRS, Machine Learning Algorithms, Probability distributions, Confidence Intervals, ANOVA, Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, Data Mining, Data Visualization, Data warehousing, Data transformation, Association rules, Clustering, Classification, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Jira, Git, GitHubOperating System Windows, Linux, Mac OSWORK EXPERIENCEMorgan Stanley, NJ Data Analyst June 2022 Current Utilized advanced analytics techniques to achieve a 15% improvement in investment returns on the data-driven investment optimization initiative at Morgan Stanley. Conducting exploratory data analysis (EDA) in Python to generate summary statistics, distribution plots, and correlation analysis, leading to a 10% improvement in predicting financial market dynamics. Achieved a 15% increase in data exploration efficiency by utilizing Power BI's cross-filtering and slicer functionality for interactive filtering of financial data based on user-defined parameters and criteria. Implementing clustering algorithms such as K-means to categorize financial assets resulted in a 15% reduction in portfolio risk and an 8% increase in average annual return. Risk-adjusted return of the portfolio is 12% higher than its systematic risk, indicating a highly efficient investment strategy. Utilized SQL queries to extract over 10,000 financial records from relational databases, including historical market data, asset prices, and transaction records. Implemented financial data processing pipelines across multiple AWS availability zones, achieving 99.99% uptime and minimizing data loss to less than 0.01%.Wipro, India Data Analyst Oct 2019 Aug 2021 Spearheaded a Wipro project targeting improved customer engagement for a retail client, resulting in a 12% rise in customer retention over a concise six-month period. Utilized Python and SQL to process and analyze an extensive dataset of over 5 million customer transaction records, uncovering crucial insights for the project. Implemented advanced segmentation techniques to identify high-potential customer segments, leading to a 15% surge in sales conversion rates. Created dynamic and interactive dashboards using Tableau to facilitate real-time visualization of key performance metrics and customer engagement patterns. Optimized SQL query techniques improved data retrieval speed by 30%. Ensured accuracy and reliability of customer transaction datasets through expertise in data cleansing techniques. Conducted A/B testing to rigorously evaluate the effectiveness of different customer engagement strategies, resulting in a 10% uplift in click-through rates.EDUCATIONMaster of Science in Information Science Sep 2021 May 2023 - New Jersey Institute of Technology, NJ, USA BTech in Electrical Engineering July 2016 May 2020 - Vellore Institute of Technology, India |
