Machine Learning Business Intelligence

Akhil Muthyam

*************@*****.*** LinkedIn +1-737-***-**** Houston, Texas

SUMMARY

Results-oriented professional with a background in cybersecurity, information systems, and cloud platforms, currently pursuing a Master's in Information Systems. Proficient in data analysis, business intelligence, cloud-based data engineering, and process automation. Skilled in Python, SQL, ETL pipelines, and machine learning techniques to extract insights and support strategic decisions. Brings a unique blend of technical and business acumen, cross-functional team collaboration, and compliance awareness. Open to roles in data analysis, business analysis, and AI or ML applications.

Core Competencies: Data Analytics and Visualization Machine Learning Fundamentals Business Intelligence and KPI Reporting SQL Querying and Data Modeling Python Scripting and Data Automation ETL Pipeline Design and Deployment Risk Analysis and Decision Support AWS, Azure, GCP Cloud Integration Agile and DevOps Collaboration Regulatory Compliance and Governance Big Data Processing and Streaming Stakeholder Communication

TECHNICAL SKILLS

Programming and Analysis: Python (Pandas, NumPy, Matplotlib, Seaborn), SQL, Advanced Excel (VLOOKUP, PivotTables, Macros)

BI and Visualization: Power BI (DAX, Power Query, RLS), Tableau; KPI dashboards and interactive reporting

ETL and Data Engineering: Apache Airflow, dbt, AWS Glue, Azure Data Factory, GCP Dataflow

Cloud and Data Warehousing: Azure (Blob Storage, DevOps), AWS (EC2, S3, Redshift), GCP (BigQuery), Snowflake, PostgreSQL, MySQL

Big Data and Streaming: Apache Spark (PySpark), Kafka, Kinesis, Azure Event Hubs

Machine Learning and Statistics: Regression, Classification, Clustering, Ensemble Methods

Domain expertise: Telecom, Healthcare, Retail, Banking

WORK EXPERIENCE

Network Security Analyst, AT&T, Texas, USA Jun 2023 – Present

Monitored and analysed structured and unstructured data across over fifty thousand endpoints, identifying patterns and reducing false positives by thirty percent.

Designed and deployed Python scripts to automate data collection, transformation, and normalization, improving business intelligence and reporting workflows.

Reduced manual data review effort by over ten hours per week, enhancing team productivity.

Created Splunk dashboards to track key performance indicators and align with organizational objectives.

Collaborated with cross-functional teams during Zero Trust implementation, improving access control visibility and reducing exposure risks by forty percent.

Integrated MITRE ATTACK framework for event classification, improving data-driven risk assessment and audit readiness.

Cyber Security Intern, Infosys, Remote Jan 2023 – May 2023

Conducted in-depth analysis of structured log data using Splunk to identify patterns and detect potential threats.

Automated AWS CloudTrail log extraction and summarized behavioural trends using Python, improving triage efficiency by thirty five percent.

Provided audit documentation and dataflow mapping for GDPR and HIPAA compliance checks.

Tuned detection thresholds based on statistical analysis, reducing false alerts by twenty percent.

Facilitated a training session focused on secure development practices, OWASP Top Ten, and data privacy fundamentals.

Site Engineer, Bhanu Builders, India Apr 2021 – Oct 2021

Managed risk, cost, and schedule for infrastructure projects exceeding half a million US dollars.

Conducted performance metric analysis leading to a fifteen percent reduction in material waste and project costs.

Streamlined workflows and reporting for internal stakeholders, ensuring quality assurance and project transparency.

Enhanced team safety awareness, resulting in a fifty percent drop in site incidents.

EDUCATION

Lamar University – Master of Science in Information Systems, May 2025

PRIST University – Bachelor of Technology in Electronics and Communication Engineering, Aug 2020

PROJECTS & RESEARCH

AI-Driven Threat Detection Using Machine Learning

Built a supervised machine learning model using Python to detect anomalies in network data from CICIDS 2017 and NSL KDD datasets.

Achieved over ninety two percent accuracy, reducing false positives by eighteen percent.

Research paper under peer review for IEEE Cybersecurity Conference 2025.

Home Cybersecurity Lab Deployment (Elastic Stack + IDS Tools)

Deployed a self-contained analytics environment using ELK stack, Snort, and Suricata to analyse malware traffic and simulate enterprise activity.

Built dashboards and custom detection rules to identify and visualize security incidents.

CERTIFICATIONS

AWS Certified Cloud Practitioner – 2024

Cisco Certified Network Associate (CCNA) – 2023

CompTIA Security+ – December 2024

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