Help & Supportcareers@gravityer.com
Data Analyst with less than a year in Python, SQL, Power BI, and Tableau, seeking an entry-level rol
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented Data Analyst with a B.Sc. in Data Science (CGPA: 9.34) and hands-on experience in Python, SQL, Power BI, and Tableau. Skilled in end-to-end data analysis including data collection, data cleaning, data wrangling, exploratory data analysis (EDA), statistical analysis, ETL pipeline development, predictive modeling, and interactive dashboard development. Proven ability to translate complex datasets into actionable business insights through data visualization and data storytelling. Experienced in machine learning techniques including K-Means Clustering, Logistic Regression, and Random Forest for customer segmentation and churn prediction. Seeking an entry-level Data Analyst or Business Analyst role to deliver measurable, data-driven impact.
Devgiri College, Chh. Sambhajinagar | Dr. Babasaheb Ambedkar Marathwada University (BAMU)
B.Sc. · Data Science
N/A – June 30, 2026
Sales Performance Analytics Dashboard
January 1, 2026 – March 1, 2026
• Collected, cleaned, and transformed 100K+ sales records using SQL queries and Pandas (ETL pipeline), achieving 98% data integrity and consistency for downstream business intelligence reporting. • Performed comprehensive exploratory data analysis (EDA) to identify monthly revenue trends, regional sales distribution, and product performance KPIs – uncovering 3 key business growth opportunities. • Designed and developed an 8-KPI interactive Power BI dashboard with drill-down filters, reducing stakeholder report review time by 40%; identified top products contributing 65% of total revenue. • Delivered data-driven recommendations on inventory optimization and targeted marketing strategies for 2 underperforming regions.
E-Commerce Churn Prediction Model
October 1, 2025 – December 1, 2025
• Engineered 20+ features from 80K+ customer transaction records using SQL and Pandas; achieved 95% data completeness through systematic data cleaning and data wrangling. • Built and optimized Logistic Regression and Random Forest churn prediction models achieving AUC-ROC of 0.87; identified inactivity and low purchase frequency as primary churn drivers via feature importance analysis. • Developed interactive Tableau dashboard visualizing at-risk customer segments and churn probability scores, enabling marketing team to prioritize retention outreach - projected 15% monthly churn reduction.
Customer Segmentation Analysis
September 1, 2025 – November 1, 2025
• Processed and cleaned 50K+ customer transaction records using SQL and Python, producing analysis-ready dataset with 95% data completeness through rigorous data wrangling and ETL processes. • Applied K-Means Clustering algorithm to segment customers into 4 distinct behavioural personas based on RFM (Recency, Frequency, Monetary) analysis, improving segmentation accuracy by 30% over manual methods. • Built 5+ self-service Tableau dashboards visualizing customer segments, spending patterns, and behavioural trends; insights supported targeted marketing campaigns improving retention strategy effectiveness by ~20%.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong alignment with a Data Analyst role, covering various aspects from data cleaning and ETL to predictive modeling and dashboard creation. The breadth of tools and techniques used (Python, SQL, Power BI, Tableau, various ML algorithms) indicates a willingness to learn and apply diverse solutions. The projects are well-structured and show an understanding of business objectives, which is a positive indicator for cultural fit in a data-driven environment. However, all projects are academic, so real-world team collaboration and handling of diverse stakeholder requirements are not yet demonstrated.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work through complex data problems, suggesting problem-solving skills. The focus on delivering business impact and reducing stakeholder review time points to a results-oriented approach. However, without direct work experience, operational fit regarding collaboration in a professional setting and handling real-world project constraints is yet to be fully assessed.