Deval Mali Email & Phone Number

Scottsdale, Arizona

Data Scientist Responsibilities: • Collaborated with data engineers and operation team to implement ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements. • Strong knowledge in Power BI on how to import, shape, and transform data for business intelligence (BI), Visualize data, author reports, schedule automated refresh of reports and create and share dashboards based on reports in Power BI desktop. • Performed data analysis by using Hive to retrieve the data from SQL to retrieve data from RedShift. • Explored and analyzed the customer specific features by using Spark SQL. • Experience in creating different visualizations and dashboards using Power BI • Performed univariate and multivariate analysis on the data to identify any underlying pattern in the data and associations between the variables. Data imputation using Scikit-learn package in Python. • Participated in features engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn preprocessing. Environment: s: AWS RedShift, EC2, EMR, S3, Python 3, Tableau Desktop (9.x/10.x), Tableau Server (9.x/10.x), Machine Learning (Regressions, KNN, SVM, Decision Tree, Random Forest, NLP, Teradata, Power BI, Agile/SCRUM

• Tackled highly imbalanced Fraud dataset using under sampling, oversampling with SMOTE and cost sensitive algorithms with Python Scikit-learn. • Wrote complex Spark SQL queries for data analysis to meet business requirement. • Worked on data cleaning and ensured data quality, consistency, integrity using Pandas, Numpy. • Participated in feature engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn preprocessing. • Improved fraud prediction performance by using random forest and gradient boosting for feature selection with Python Scikit-learn. • Performed Naïve Bayes, KNN, Logistic Regression, Random forest, SVM and XGboost to identify whether a loan will default or not. Environment: MS SQL Server 2014, Teradata, ETL, SSIS, Alteryx, • Tackled highly imbalanced Fraud dataset using under sampling, oversampling with SMOTE and cost sensitive algorithms with Python Scikit-learn. • Wrote complex Spark SQL queries for data analysis to meet business requirement. • Worked on data cleaning and ensured data quality, consistency, integrity using Pandas, Numpy. • Participated in feature engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn preprocessing. • Improved fraud prediction performance by using random forest and gradient boosting for feature selection with Python Scikit-learn. • Performed Naïve Bayes, KNN, Logistic Regression, Random forest, SVM and XGboost to identify whether a loan will default or not. Environment: MS SQL Server 2014, Teradata, ETL, SSIS, Alteryx, Tableau, Machine Learning (AWS Redshift, MapReduce, HDFS, SharePoint , Machine Learning (AWS Redshift, MapReduce, HDFS, SharePoint

• Gathered, analyzed, documented and translated application requirements into data models and Supports standardization of documentation and the adoption of standards and practices related to data and applications. Developed many Tabular Reports, Matrix Reports, drill down Reports, Bar Graph Reports using Power BI. • Participated in Data Acquisition with Data Engineer team to extract historical and real-time data by using Sqoop, Pig, Flume, Hive, MapReduce and HDFS. • Wrote user defined functions (UDFs) in Hive to manipulate strings, dates and other data. • Performed Data Cleaning, features scaling, features engineering using pandas and numpy packages in python. • Applied clustering algorithms i.e. Hierarchical, K-means using Scikit and Scipy.Environment: Machine learning (KNN, Clustering, Regressions, Power BI, Random Forest, SVM, Ensemble), Linux, Python R, Tableau, Oracle 11g, SQL Server 2012

Master Of Science In Data Science, Data Science

Bachelor Of Engineering In Computer Engineering, Computer Engineering