Data Analyst | Computer Vision Engineer | Dynatrace Implementation Professional

Experienced Data Analyst, Computer Vision Engineer, and Dynatrace Implementation Professional with a background in Web Development, skilled in Python, SQL, OpenCV, and Deep Learning. Passionate about AI-driven solutions, machine learning, and full-stack development for impactful business insights. Porfolio Projects Directory

Technical Skills: Python, R, NumPy, Pandas, Matplotlib, Seaborn, SQL, Tableau, Power BI, Google BigQuery, SKLearn, MS Excel, Git, HTML, CSS, JavaScript, React.js, .NET Framework, SharePoint Development & Integration, Vite, TensorFlow, PyTorch, OpenCV, Predictive Modeling, PostgreSQL, AWS, Google Cloud (Colab and BigQuery), CI/ CD,GitHub Actions.

Other Skills : Economics, Psychology, Data Preprocessing, Data Wrangling, Data Plotting, Linear Regression Modeling, Detail-Oriented

Education

• Bachelor’s in Computer Science

  S.R.M University (June 2018)

Work Experience

Innovacle Technologies | AI & Computer Vision Engineer (June 2023 - February 2025)

• Developed an AI-powered tile overlay system for interior design using OpenCV and deep learning
• Built a real-time deepfake detection model for identifying manipulated videos.
• Automated vanishing point estimation for perspective alignment in computer vision applications.

Innovacle Technologies | Data Analyst (December 2021 - June 2023)

• Successfully delivered multiple client projects involving data analysis, reporting, and Excel-based insights, enhancing data-driven decision-making for businesses.
• Developed and implemented data quality checks, reducing data errors by 15 percent and improving overall accuracy.
• Conducted statistical analysis and predictive modeling to forecast revenue trends.
• Applied machine learning models for anomaly detection and customer segmentation.
• Collaborated with cross-functional teams to gather requirements, analyze data, and present findings in a clear and concise manner, contributing to increased client satisfaction.

Mytravaly.com | Web Developer(September 2019 - June 2021)

• Led the development and deployment of the company’s travel website, integrating full-stack features for enhanced functionality.
• Worked with .NET Framework and Django to develop backend services, improving system performance.
• Developed and maintained responsive web applications using React.js and Django.
• Implemented API integrations for dynamic content and real-time data updates.
• Implemented optimized API integrations, ensuring real-time synchronization between the website backend and the Android app.

Certified Dynatrace Implementation Professional

Verify Here Certified in Dynatrace Implementation, I specialize in designing and deploying observability solutions that align with business and technical requirements. My expertise includes:

• Architecture & Planning –Structuring Dynatrace environments to support scalability, security, and multi-cloud or hybrid deployments.
• Platform Implementation – Installing, configuring, and integrating Dynatrace components including OneAgents, ActiveGates, and extensions.
• Data Ingestion & Management – Enabling and optimizing the flow of metrics, logs, traces, and real user data for comprehensive observability.
• Optimization & Governance – Establishing management zones, alerting profiles, and dashboards that deliver actionable insights without unnecessary noise.
• Business Value Realization – Leveraging Dynatrace capabilities to improve application performance, reduce MTTR, and provide transparency across IT and business teams.

Certified Dynatrace Associate

Verify Here Recognized for foundational expertise in application performance monitoring (APM) and observability using the Dynatrace platform.

Projects

Telco Customer Churn Analysis and Retention Strategies

Github

In this project, I analyzed customer churn data to identify factors influencing customer retention. I used Python along with libraries like Pandas and NumPy for data cleaning and manipulation, Matplotlib and Seaborn for visualization, and SciPy for statistical testing. The analysis involved exploring relationships between customer attributes such as gender, online security, and churn status. Although a chi-square test showed no significant association between gender and churn, further analysis revealed that customers without online security were more likely to churn, with 20% of women and 21% of men unsubscribing. This project demonstrates how exploratory data analysis and statistical methods can provide actionable insights for addressing business challenges like customer churn.

Analysis of Y Combinator-Funded Startups

Github

Analyzed and visualized factors associated with the success and failure of 4,845 Y Combinator-funded startups using Python libraries (Pandas, Matplotlib, Plotly, Seaborn). Merged datasets on companies, industries, regions, and founder education to create a comprehensive dataset, transforming categorical data into numerical representations with cat.codes to identify correlations. Explored success factors such as industry, region, and team size, defining success as startups with 100+ employees and active status. Visualized findings with stacked bar charts, tree maps, and sunburst plots to compare trends between successful and failed startups, uncovering regional and sector-specific insights into startup outcomes.

Analysis of Bitly User Data for USA.gov

Github

Analyzed and visualized user data on government-related URLs shortened via Bitly, using Python libraries (Pandas, Seaborn, Matplotlib) to extract and process JSON data. Identified top time zones and usage patterns, categorized users by OS type (Windows vs. non-Windows), and grouped data by time zone and OS for detailed insights. Normalized and visualized data to highlight regional and platform-specific trends in service activity.

Fandango Score Comparison

Github

Analyzed movie ratings across multiple platforms to identify discrepancies and correlations, focusing on Fandango and Metacritic ratings. Applied statistical methods to calculate central tendencies and performed regression analysis using SciPy’s stats module, evaluating model accuracy with R-squared, mean absolute error, and root mean squared error. Visualized linear regression results and detected outliers, showcasing expertise in data cleaning, statistical analysis, and regression modeling.

Certifications

• Dynatrace Implementation Professional ,2025. Certificate
• Dynatrace Associate ,2025. Certificate
• JSON and Natural Language Processing in PostgreSQL: University Of Michigan ,2024. Certificate
• Excel Skills for Business Specialization: Macquarie University, 2024. Certificate
• PostGreSQL Specialization: University Of Michigan , 2024. Certificate
• SQL for Data Science: University of California, Davis , 2023. Certificate
• Google Data Analytics Certification, 2023. Certificate