Help & Supportcareers@gravityer.com
AI Engineer with less than a year in data analysis, machine learning, and business intelligence.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven Computer Science undergraduate specializing in AI & ML with hands-on experience in Python, SQL, data analysis, and machine learning. Proficient in Power BI for building interactive dashboards and translating complex datasets into actionable business insights. Skilled at working with large-scale datasets, building end-to-end data pipelines, and presenting findings to both technical and non-technical audiences.
Malla Reddy University
B.E. · Computer Science Engineering (AI & ML)
September 1, 2022 – May 1, 2026
HR Employee Attrition Analysis
June 1, 2026 – Present
Performed exploratory data analysis on the IBM HR Analytics dataset (1,470 employees, 35 features) to identify key drivers of employee attrition. Built visualizations using Seaborn and Matplotlib segmented by department, tenure, job role, and satisfaction score, surfacing actionable insights for HR stakeholders. Created a Power BI dashboard with interactive filters for department, attrition reason, and tenure bands, enabling self-serve reporting for business teams.
E-Commerce Sales Performance Dashboard
June 1, 2026 – Present
Analyzed 100K+ real orders from the Brazilian E-Commerce (Olist) dataset using Python (Pandas) to clean, merge, and engineer features across 6 relational tables, handling nulls, duplicates, and multi-installment payment aggregation. Designed and built an interactive Power BI dashboard tracking $15.4M in revenue, order volume trends by quarter, top product categories, and payment method distribution to support data-driven business decisions. Produced a master dataset of 110K rows × 28 columns through a structured 7-phase pipeline covering data loading, cleaning, merging, feature engineering, KPI validation, and CSV export.
Customer Churn Prediction + Dashboard
June 1, 2026 – Present
Built a churn prediction model on the Telco Customer Churn dataset (7,043 customers, 21 features) using Logistic Regression and XGBoost, achieving 82%+ accuracy. Generated a Feature Importance chart using Scikit-learn to communicate top churn drivers (contract type, tenure, monthly charges) to non-technical stakeholders. Developed a Power BI dashboard showing churn probability segments, risk tiers, and key contributing factors for business team consumption.
AWS Academy Cloud Foundations
AWS Academy
June 1, 2026 – Present
Programming in Java
NPTEL
June 1, 2026 – Present
Data Analytics Bootcamp
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong interest in data-driven problem-solving across different domains (e-commerce, HR, telecom churn). This indicates a proactive learning attitude and a willingness to apply skills to diverse business challenges. The target role of 'AI Engineer' aligns with the candidate's specialization in AI & ML and relevant coursework. However, the lack of professional experience or team-based projects makes it challenging to fully assess cultural fit beyond academic alignment.
Soft Skills & Operational Fit
The candidate demonstrates an ability to translate complex data into actionable business insights, which is valuable for operational impact. The project descriptions indicate a structured approach to data pipelines and a focus on communicating findings to both technical and non-technical audiences. However, without direct work experience, it's difficult to assess collaboration, stress handling, or direct operational fit in a professional setting.