Data Analyst Machine Learning

Kumara Raja Ankam

Data Analyst

623-***-**** AZ, USA ****************@*****.*** LinkedIn

SUMMARY

Data Analyst with 5 years of expertise in leveraging Python, SQL, and data visualization tools like Tableau and Power BI to extract actionable insights from complex datasets.

Proficient in statistical analysis, predictive modeling, and dashboard development, contributing to significant efficiency gains and cost reductions.

Experienced in working with cloud platforms (AWS, Azure) and ERP Planning & CRM systems (SAP, MS Dynamics Nav, Salesforce, HubSpot).

Skilled in automating data processes, conducting EDA, and implementing machine learning algorithms to drive informed decision-making.

Proven track record of collaborating with cross-functional teams and delivering valuable insights to drive business growth. Strong analytical, problem-solving, and communication skills, adept at translating data into actionable recommendations.

Experience in Payments, Data Profiling, Data Validation, and Sanctions/Compliance within the Banking Industry.

EDUCATION

Master of Science in Information Technology

Arizona State University, AZ, USA

Bachelor of Technology in Computer Science

SRM Institute of Science and Technology, Chennai, India

TECHNICAL SKILLS

Languages: Python, Scala, R

Packages: NumPy, Pandas, Matplotlib, Seaborn, ggplot2, SciPy, Scikit-learn, TensorFlow

Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP)

IDEs: Visual Studio Code, PyCharm, Jupyter Notebook

Database Management: MySQL, PostgreSQL, SQL, Oracle

Cloud Technologies: AWS, Azure

Methodologies: SDLC, Agile, Waterfall, Predictive Modeling

Version Control: Git, GitHub, Bitbucket

Data Manipulation: Data Analysis, Data Mining, Data Preprocessing, Data Mapping, Data Cleaning, Data Visualization, Data Modeling, Data Warehousing, Data Storytelling, Data Wrangling, Data Acquisition, Data Integration, Data Transformation

Other Skills: SSIS, SSRS, Machine Learning Algorithms, CI/CD, Probability distributions, Confidence Intervals, ANOVA, Data Mining, Data Storytelling, Association rules, Clustering, Advanced Analytics, ETL Processes, Clustering, Informatica, Report Generation, Regression, A/B Testing, Forecasting & Modeling, Hypothesis Testing, Time Series Analysis, Root Cause Analysis

Additional Skills: Payments Data Analysis, Sanctions/Compliance, Data Profiling, Data Validation, Defect Tracking, Requirements Management, Agile Methodologies

WORK EXPERIENCE

Data Analyst CVS Health, AZ Aug 2022 - Present

Leveraged SQL to efficiently extract and preprocess data from diverse healthcare databases, ensuring data integrity and reliability for subsequent analysis.

Utilized Python libraries such as Pandas and NumPy to conduct in-depth data analysis, uncovering trends and patterns within large datasets to inform strategic decision-making.

Developed and validated predictive models using Scikit-Learn, achieving a 15% reduction in patient readmissions by identifying high-risk individuals and enabling proactive interventions.

Implemented stringent data validation protocols within the ETL (Extract, Transform, Load) pipeline, resulting in a 30% reduction in data errors, ensuring data integrity and reliability across healthcare analytics and reporting.

Applied data-driven insights to enhance patient outcomes by 20%, optimizing treatment plans and resource allocation based on predictive analytics.

Utilized Data Analysis Expressions (DAX) in Power BI for advanced data calculations and aggregations, enhancing the depth of insights derived from the data.

Leveraged AWS Kinesis for real-time data streaming and processing, providing timely insights into patient health metrics.

Implemented Apache Kafka to facilitate real-time data ingestion from various healthcare systems, ensuring that data was promptly available for processing and analysis.

Conducted Payments-related Analysis and worked closely with cross-functional teams to ensure compliance with payment processing and fraud detection systems.

Collaborated in Sanctions/Compliance-related Projects within the healthcare domain, identifying potential risks in financial transactions and ensuring data integrity in alignment with regulatory standards.

Data Analyst Capgemini, India Jul 2018 - Aug 2021

Employed Python extensively to conduct thorough data analysis across various business sectors such as sales, marketing, and customer interactions which provided in-depth insights into performance metrics and trends, enhancing overall business understanding.

Performed Payments Data Analysis by gathering and validating business requirements, developing SQL queries to extract meaningful insights, and ensuring that payment systems complied with regulatory standards.

Conducted Data Profiling and Data Validation for large datasets, ensuring the accuracy and quality of data used in reports and decision-making processes.

Designed and implemented data pipelines using SSIS, supporting faster data integration, transformation, and payment system monitoring.

Contributed to Sanctions/Compliance Projects within the banking industry, performing thorough analysis of financial transactions and ensuring adherence to compliance standards, including regulatory screening for potential sanctions risks.

Utilized Agile methodologies to manage and track project progress, resolving defects and ensuring requirements were met throughout the project lifecycle.

PROJECTS

Payments Data Analysis and Compliance Project

Worked on analyzing payments data across multiple domains, ensuring compliance with payment systems, fraud detection protocols, and regulatory standards.

Developed SQL queries to identify transaction anomalies and assessed the financial impact of potential fraud.

Implemented data validation and profiling techniques to maintain data accuracy and reliability within payment systems.

Sanctions and Compliance Monitoring Project (Banking)

Collaborated with cross-functional teams to ensure that banking transactions adhered to sanctions regulations and AML compliance requirements.

Utilized SQL, Python, and data profiling techniques to validate transaction data and flag suspicious activities.

Automated data extraction and analysis processes, reducing the time required for compliance checks and reporting by 30%.

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