Data Science Engineer

AKSHITH GOUD KASIPURAM

Chicago, IL 773-***-**** **********@****.***.*** linkedin.com/in/akshith-goud SUMMARY

Data Science graduate student with 3+ years of experience in data analytics, automation, and performance reporting at Infosys. Skilled in Python, SQL, Power BI, and machine learning to extract insights and support data-driven decisions. Strong background in dashboard development, data transformation, and statistical analysis. Seeking to apply analytical expertise in a data analyst internship focused on solving real-world business challenges. EDUCATION

Masters of Applied Science (Data Science), GPA:3.66/4.0 Expected December 2025 Illinois Institute of Technology, Chicago, IL

Bachelor of Technology (B. Tech), GPA: 3.78/4.0 June 2017 - June 2021 SRM Institute of Science and Technology, Chennai, India SKILLS

• Programming & Tools: Python (Pandas, NumPy, Scikit-learn), SQL, Git, Jenkins, JIRA, ServiceNow.

• Database Management: MySQL, PostgreSQL.

• Data Visualization: Power BI, Matplotlib, Seaborn. WORK EXPERIENCE

Senior Systems Engineer

Infosys Limited, Hyderabad, India (Hybrid) October 2022 - November 2023

• Designed and streamlined 5+ ServiceNow dashboards to track operational KPIs, including incident counts, resolution times, severity trends, and root causes, providing real-time visibility for leadership decision-making.

• Developed specialized aging incident escalation views, enhancing SLA compliance by 20% and minimizing unresolved backlogs by 25% across multiple operations teams.

• Analyzed 3,000+ telecommunications incident and operational records to identify recurring failure patterns and trends.

• Initiated process improvements based on insights, resulting in a 25% decline in repeat incidents through effective Jira-based project management.

Systems Engineer

Infosys Limited, Hyderabad, India (Hybrid) July 2021 - September 2022

• Crafted and refined SQL/MySQL queries for operational data extraction and transformation, leveraging Python

(Pandas) to preprocess and clean 50K+ records monthly, supporting business analysis and ML model training.

• Monitored daily data flows across 3+ production data pipelines, ensuring 50K+ records were accurately processed and delivered to real-time ML models with a 98%+ success rate.

• Evaluated F1 Score, Precision, and Recall across 5+ production ML models, consistently maintaining thresholds

0.85 in 95% of evaluations to ensure operational quality.

• Investigated operational data to identify recurring issue patterns, performed root cause analysis, and collaborated with teams to implement process improvements, decreasing repeat incidents by 20%. Data Science Intern

Infosys Limited, Hyderabad, India (Hybrid) February 2021 - May 2021

• Developed a full-stack application using Spring Boot to simplify project deployment, integrating repository management and user input-based deployment customization.

• Automated deployment workflows using CI/CD pipelines with Jenkins, reducing manual intervention by 40% and increasing deployment efficiency while mitigating downtime.

• Designed intuitive user interfaces based on data-driven design principles, elevating user engagement by 25% and boosting data collection quality.

• Led daily Scrum ceremonies including stand-ups, sprint planning, and retrospectives, leveraging sprint performance data to strengthen collaboration efficiency and maintain smooth project progress. PROJECT EXPERIENCE

World Happiness Report Analysis & Machine Learning Modeling May 2024 - August 2024

• Merged and standardized World Happiness datasets (2015–2019) with schema variations, creating a unified analytical base facilitating efficient machine learning model training and streamlined analysis across 150+ countries.

• Conducted statistical analysis (heatmaps, boxplots) on 500+ global records across 150+ countries, identifying GDP, Health, Freedom, and Social Support as key drivers impacting World Happiness scores.

• Trained and fine-tuned regression models (Random Forest, Gradient Boosting, XGBoost), achieving an R score of 0.795 and RMSE of 0.500 for happiness score prediction.

• Applied SHAP to interpret model outputs, amplifying model transparency and enabling actionable insights for 95% of predictions through global and local feature importance explanations.

• Created an interactive Power BI dashboard to visualize happiness rankings and GDP insights, delivering clearer data communication and accelerating data-driven decision-making. CERTIFICATIONS

• SQL for Data Science.

• Python 3 Programming Specialization.

• Google Data Analytics Professional Certificate.

• IBM IT Scrum Master Professional Certificate.

COURSES

• Big Data Technologies, Business Statistics, Statistical Learning, Regression Analysis, Time Series Analysis.

Contact this candidate