- Dedicated and detail - oriented Data Analyst with 5 years of experience on building growth strategies, extracting insights from data, automating regular growth hacks, designing and executing A/B testings, creating machine learning models for driving efficient growth in R-Studio.
- Experience and ownership of full Machine Learning pipeline: data collection, feature engineering, model building, parameter tuning, deployments of classification, regression and clustering models on structured data, semi-structured data, unstructured data and text data in R-Studio.
- Created Power BI dashboards by using Scatter Plots, Geographic Map, Line charts, Pie chars, Bar charts and Density charts to identify the hidden key points and track all the important metrics and extract insights, understand the strengths and weaknesses at those points and come up with solution, then build models to improve KPI performance.
- Pulled data from Azure and created the dashboards, reports and visualizations in Power BI, and ask questions to get visualizations in response.
- Mainly focus on the engagement journey of all type of customers and built strategies for retention, revenue, sales and efficiency. Areas such as: Growth Models and strategy, Customer Engagement, Retention and Churn, Automation, Data Analysis and Metrics, Machine Learning Models, A/B Testing.
- Exceptional organizational, time management skills and documentation skills and expert in core revenue management concepts such as demand forecasting, yield management, pricing, etc.
- Professional experience in business intelligence and marketing strategies, including deriving business insights, improving business growth and customer engagement.
- Outstanding written, verbal and presentation skills with strong analytical and problem-solving skills to address recommending solutions. Effectively communicate with external clients and internal teams to deliver solutions in timely fashion.
Programming Languages: SQL, Python, R, SAS
Database: Relational MySQL, SQL, MSSQL, PostgreSQL, NoSQL MongoDB, DynamoDB, Cosmos DB
Tools: MySQL, Jupyter Notebook, PyCharm, Weka, Workbench, RStudio, Matlab
Big Data: Hadoop(HDFS, HBase, MapReduce, Hive, Apache Pig, Spark), AWS(Redshift, EMR, S3, Glue, RDS, EC2), GCP (Kubernetes, BigQuery), Microsoft Azure
ETL Tools: MS SQL BI(SSIS, SSAS, SSRS), AWS(GLUE, Redshift), Hadoop(HiveQL, SparkSQL), Informatica
Statistics & Machine Learning: A/B test, Linear Models, Regression models, Time Series, Logistic Regression, PCA, SVM, Decision Tree, Random Forest, Boosting and Bagging, XGB, K-NN, K means clustering, CNN, RNN, DNN, NLP, Scenario Analysis
Cloud: AWS (EC2, S3, EBS, RDS, EMR, IAM, etc.), GCP (kubernetes), Docker, Microsoft Azure
Data Visualization : Tableau, Python(Matplotlib, Plotly, Seaborn), R-Studio (ggplot2), Power BI, Chartio, Google Data Studio, Matlab
Management: A gile(JIRA), Airflow, GitHub, Git, Docker, S lack
Others: basic & advanced MS Excel(formulas, conditional formatting, macros, lookups, VBA, pivot tables and graphs), MS office, Teradata, Google Analytics
- Use EAD, statistical inference to analysis customer information and data visualization to track and predict customers behavior.
- Use training set, test set, cross-validation and ROC curve to choose the best model and parameters to identify fraudulent activity for bank to check fraud, use A/B test to test website optimization to increase click through rate.
- Use A/B test and machine learning to do funnel analysis to increase conversion rate and revenue for e-commerce and retail
- Use click stream analysis for web activity analysis, market research, and analyzing employee productivity in e-commerce industry.
- Involved in creating customer transaction web page for company and mainly focused on data visualization and building statistic predictive models on providing insights for enhancing price prediction and enhancing customer transaction rate.
- Use Hadoop, HDFS, MapReduce, Hive, Pig and Spark to manage data processing in AWS (EMR) and stored big data in AWS (S3) as our personal marketing website. B ug requests, resolve SQL performance issues in AWS (RDS).
- Used SQL and HiveQL(Hive/Spark) to to clean data for missing value removal, duplicate removal and to apply normalization and denormalization processes and load into HBase.
- Assisted in creating various schemas based on business analytics needs by SQL/HiveQL.
- Performed HiveQL to pull data from HBase to build machine learning models and to implement business analysis.
- Hands on experience of using workflow tools like Jira to view, manage, and report for marketing data.
- Worked extensively through Python and R with AWS on calculation and through Power BI to create visualization and reports.
- Organized, scheduled, facilitated meetings with business teams for documenting data dictionaries and data quality control assessments onto templates.
- Performed routine data analysis based on project specifications.
- Created database objects(tables, views, procedures) using MySQL to provide definition, structure and to maintain data efficiently.
- Created Power BI dashboards and performed EDA to uncover trading trends and relationships of every precious metal futures.
- Advised the supervisor of factors that might affect the quality and usefulness of the data.
- Created dashboards on Power BI using functions, parameters, sets, groups and identified key metrics and performance indicators(KPI) for predictions and analysis.
- Extensively created interactive dashboards and applied actions(filter, highlight and URL) to dashboard to prepare user stories and deliver actionable insights in Power BI.
- Built machine learning models for each one of precious metals: time-series models, Polynomial Regression, SVM to make predictions on the metal prices in Python.
- Applied statistical models(A/B test, Hypothesis test, regression) to understand customer preferences on the web page setup.
- Prepared project progress and status reports and submitted to the management team.
- Built moderately complex reports and visualizations to support tier ad-hoc reporting requests.
- Provided BI analysis for the marketing team to review impact on key metrics with Microsoft PowerPoint and Power BI dashboard.
- Contributed to the delivery of quality contract deliverable based on requirements and timeline.
- Gather business requirements from business users, stakeholders, SMEs with market teams.
- Worked with team of developers, designed, developed and implemented a BI solution for Sales, Product and Customer KPIs.
- With the building NLP content based analysis, created machine learning rule-based predictions on the recommendation products for customers and increased sales by 15%.
- Worked mainly with Python scripting to code predictive models and data wrangling.
- Monitored and analyzed daily/monthly/quarterly/yearly sales report to understand product and market trends.
- Worked with data engineering teams in migrating data stored in AWS.
- Used SQL to clean data for missing value removal, duplicate removal and to apply normalization and denormalization processes.
- Assisted in creating various schemas based on business analytics needs by SQL.
- Performed SQL to pull data from AWS to implement business analysis.
- Done advanced SQL queries such as subqueries, join, union, window function(DENSE RANK/RANK/LAG/LEAD), procedure storage, view creation.
- Created Python-based data visualization with packages Matplotlib, Seaborn, Plotly for structured datasets and apply EDA.
- Worked with management and team members to clarify visualization requirements and business strategic report requirements.
- Create Power BI dashboard to measure impacts and performances of various growth initiatives including incremental lift, revenue, and business metrics.
- Conducted Market Research, Feasibility Studies, Data Analyses, Data Mapping, Risk Identification, Risk Assessment, Risk Analysis and Risk Management.
- Assessed data quality using custom-built assessment metrics.
- Applied A/B test on insights from R created visuals to visualize data in Power BI.
- Developing testing plans to support new feature implementation after passing A/B test.
- Used machine learning technique PCA to reduce the dimensionality of the features of the customer information by capturing the most variance data.
- Applied machine learning models such as Logistic Regression, XGBoost, Decision Tree, Random Forest with GridSearchCV by tuning hyper-parameters to draw conclusions for strategic business improvement actions.
- Participated applying CNN on image data analysis with Spark, AWS EC2.
- Provided data-driven insights to enable decision-making for products and market developments.
- Created effective market related strategies and promotion plans to retain current customers.
- Interpreted data to draw managerial strategy conclusions and reported to manager with MS PowerPoint, Power BI and the resulting customer service usage increased by 23%.
- Assisted in Design of project-related management pipeline with Apache Airflow, Linux(Ubuntu).
- Utilized Git as version control tool and JIRA as project tracking tool for team collaboration.