Machine Learning Business Intelligence

Chaitanya Anudeep Origanti

Boston, MA ********.*@************.*** +1-475-***-****

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PROFESSIONAL SUMMARY

Results-driven Engineer with over 3+ years of experience in data analytics, engineering, and business intelligence, based in Boston, MA. Specializes in ETL pipeline enhancement, SQL query optimization, and data visualization using Power BI and Tableau. Proficient in Azure Data Factory, Databricks, Apache Airflow, and Power Platform. Demonstrating expertise in developing advanced visualizations, optimizing large datasets, and working cross-functionally with engineering teams and program managers. Proven track record in migrating data pipelines, deploying machine learning models, and creating impactful data products. Dedicated to refining BI and visualization spaces to deliver world-class analytics and actionable insights. EDUCATION

Northeastern University, Master of Science in Analytics Expected July 2024

Coursework: Fundamental of AI, Intermediate Analytics, Decision Support and Business Intelligence Boston, USA

Amrita Vishwa Vidyapeetham, bachelor’s in technology in Electronics and Communication July 2016 - July 2020

Coursework: Introduction to Data Structures and Algorithms, Digital Communications Kollam, India

TECHNICAL SKILLS

Programming Languages: Python (Pandas, Keras, Scikit-learn, PyTorch, PySpark, NumPy, Matplotlib, TensorFlow), R, Java, Scala, PowerShell

Technologies: ETL, Kafka, Snowflake, Power Platform, SharePoint, Office Scripts, VBA, Ansible, GIT, Active Directory, RESTful / SOAP APIs

Databases: MySQL, MS SQL Server, PostgreSQL, Oracle DB, Dataverse, ComsoDB, Azure Storage, AWS S3 Frameworks/Tools: Power Apps, Power Automate, Excel, Word, Power BI, Tableau, Looker, JIRA, Azure Data Factory, Azure Machine Learning, Databricks, Airflow, Azure Logic Apps, AWS Glue, Big Query, Docker, EC2 CERTIFICATES

Microsoft Azure Data Engineer Associate (DP-203) Microsoft Power Platform App Maker Associate

(PL-100) Microsoft Azure AI Fundamentals (AI-900) CCNA Routing and Switching: Introduction to Networks

PROFESSIONAL EXPERIENCE

Data Engineer, PricewaterhouseCoopers LLP June 2021 – December 2022 Bangalore, India

• Leveraged API and Kafka technology to ingest and transform data, enhancing the robustness and scalability of the data pipeline. Implemented Databricks for advanced data processing and analytics, and Azure Data Factory for orchestrating complex ETL workflows. This integration enabled efficient data ingestion, transformation, and loading processes, reducing manual intervention.

• Elevated data insights and reporting capabilities by integrating Power BI. This enabled the creation of interactive and comprehensive dashboards, facilitating better data visualization and decision-making for stakeholders.

• Achieved notable improvements in data processing speed by refining SQL queries, implementing indexing on Delta tables, and restructuring database schemas for optimal performance. This resulted in faster query execution and reduced latency.

• Led the development and deployment of a predictive analytics model using Azure Machine Learning. This model improved data-driven decision-making accuracy by 15%, allowing the organization to adopt more proactive and strategic business approaches.

• Utilized Docker and Kubernetes for containerization and orchestration of applications, ensuring scalable and reliable deployment environments. This approach enhanced the efficiency of resource management, simplified the deployment process, and increased the overall resilience of the infrastructure. Data Analyst, Infosys India limited. August 2020 - June 2021 Mysore, India

• Conducted in-depth system and user interaction analyses using SQL and Excel, extracting essential KPIs and insights that led to significant improvements in system efficiency and user satisfaction.

• Created and managed over 50 Power BI dashboards to track key performance indicators, providing clear and actionable insights for decision-makers.

• Efficiently converted over 100 Excel files to JSON format, greatly enhancing data interoperability and operational flexibility.

• Streamlined data processes and reporting workflows, contributing to a more agile and responsive operational environment.

Machine Learning Researcher, Northern Illinois University July 2019 - June 2020 Dekalb, USA

• Leveraged machine learning algorithms in MATLAB to enhance signal processing, achieving a 15% increase in bandwidth efficiency and a 20% reduction in power consumption.

• Achieved a 10% reduction in Mean Squared Error (MSE) and a 5% decrease in Bit Error Rate (BER), significantly improving overall signal quality.

• Contributed to the authorship of three research papers published in IEEE Xplore, showcasing the advancements and findings in signal processing efficiency. RELEVANT PROJECTS

E-commerce-Sales-Analysis-Pipeline for Sales Forecasting & Customer Insights (GitHub)

• Developed an advanced data engineering pipeline for e-commerce sales analysis, utilizing Apache Airflow for orchestration and Click House as the analytical data warehouse, ensuring reliability, scalability, and real-time insights generation from vast datasets.

Big Data Analytics Project: IPL Data Analysis with PySpark (GitHub)

• Orchestrated data preprocessing to ensure quality, harnessed PySpark for analysis with SQL queries, employed Matplotlib/Seaborn for visualization. Established Spark environment, imported IPL datasets from AWS S3, executed code in Databricks, and reviewed results diligently. Performance Analysis of SM vs. OFDM in Varied Channel Models

• Leveraged machine learning algorithms in MATLAB to conduct a comparative analysis of SM and OFDM, achieving enhanced spectral efficiency (4.2 bits/s/Hz for SM) and power savings (15% for OFDM), and significantly reducing MSE by 30% for SM and 25% for OFDM, showcasing a blend of advanced signal processing and machine learning expertise.

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