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Data Analyst with 2+ years in data manipulation, visualization, and predictive modeling.
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Mohammed Zain is a dedicated Data Analyst with 2.0 years of experience, proficient in data cleaning, SQL, Python, and Power BI. He has a strong background in developing predictive models for loan approvals and customer churn, as well as building robust web scraping and ETL pipelines. His expertise spans data manipulation, visualization, and strategic business initiatives aimed at enhancing data accuracy and driving informed decision-making.
SRN Adarsh Group of Institution
Bachelor Of Computer Application · Computer Application
August 1, 2020 – June 30, 2024
Metvy
Data Analyst,Certification
August 1, 2025 – September 1, 2025
India
Staragile
Operation Lead Growth Specialist
October 1, 2024 – Present
India
Rubixe | Innovative AI Tech Solutions
Web Scrapping Intern
April 1, 2024 – May 1, 2024
India
Loan Approval Prediction
June 28, 2026 – Present
Problem Statement: Financial institutions need to minimize risk and reduce manual effort in loan processing. This project aimed to build a predictive model to automate loan eligibility. Process and Tools: Pre-processed and engineered features from the applicant dataset using Python libraries (Pandas, NumPy). Trained and evaluated multiple classification models (Logistic Regression, Random Forest). Impact: The final model achieved a 92% accuracy on the test set in predicting loan status. It demonstrated the potential to reduce manual review time by an estimated 40% by automatically identifying clear approval or rejection cases.
Customer Churn Prediction for Food Delivery Platform
June 28, 2026 – Present
Problem Statement: High customer churn increases acquisition costs and reduces lifetime value. This project aimed to build a model to proactively identify customers at high risk of churning. Process and Tools: Analyzed historical customer data using SQL and Python (Pandas, NumPy) to engineer key features like order frequency and recency. Trained and compared classification models (Logistic Regression) and visualized key risk factors on an interactive Tableau dashboard. Impact: The final model predicted churn with 88predictor. This insight enabled a targeted retention strategy projected to reduce monthly churn by 15
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to applying data science techniques to real-world business problems (finance, e-commerce). The internship experiences, particularly the web scraping role, show a willingness to tackle challenging technical tasks and deliver measurable results. The 'Operation Lead Growth Specialist' role, while a departure from a pure data analyst path, indicates adaptability and experience in a business-oriented environment. The combination of academic projects and internships suggests a foundational understanding of data analysis principles and tools, aligning with a data-driven culture. However, the limited professional experience directly as a Data Analyst (only a short certification internship) means the depth of cultural fit within a dedicated data team is yet to be fully proven.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to identify business problems, propose data-driven solutions, and articulate the impact of their work. The web scraping internship highlights initiative and problem-solving in overcoming technical challenges. The 'Operation Lead Growth Specialist' role, while not directly technical, suggests experience in business development and client management, which could be beneficial for understanding business needs in a data analyst role. However, the current role's description lacks specific data analysis responsibilities, which might indicate a shift in focus or a gap in recent hands-on technical work.