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Data Science with less than a year in Data Cleaning, EDA & ML
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Science & Analytics graduate with hands-on experience in data cleaning, exploratory data analysis, machine learning, and dashboard development. Skilled in Python, SQL, Power BI, and NLP-based applications. Experienced in building end-to-end projects involving frontend, backend, databases, and analytics to convert raw data into meaningful business insights.
Malla Reddy University
B.Tech · Computer Science & Engineering (AI & ML)
August 1, 2020 – June 30, 2024
Srichaitanya Junior College
Board of Intermediate Education
June 1, 2018 – May 31, 2020
Gorkey Public School
Secondary (CBSE)
N/A – Present
Ramana Soft
Data Science (Internship)
November 1, 2025 – Present
India
Doc Play- A Tool play with Multiple Documents using Natural Language Processing
June 1, 2026 – Present
Developed an NLP-based system to search and query multiple documents such as PDFs and Word files Implemented semantic search using embeddings and FAISS vector database Designed backend logic for document chunking, embedding generation, and similarity search Built a Streamlit frontend for document upload and natural language queries Reduced document search time by 70% compared to manual methods.
Sales & Marketing Analytics Dashboard (Power BI – Real-Time Project)
June 1, 2026 – Present
Built an end-to-end Sales & Marketing dashboard using Power BI on 4 years of historical data. Performed data cleaning and transformation using Power Query (handled missing values, formatting, and relationships). Designed a star schema data model with fact and dimension tables for optimized performance. Created interactive dashboards to analyze revenue, profit, units sold, product performance, and sales rep effectiveness. Implemented slicers for Country, Year, and Month to enable dynamic business analysis. Identified top-performing products, high-revenue categories, and best sales representatives. Enabled management to track KPIs such as Total Revenue, Gross Profit, Units Sold, and Average Revenue.
Real Estate Investment Advisor
June 1, 2026 – Present
Performed data cleaning, feature engineering, and exploratory data analysis (EDA) on large real estate datasets. Engineered domain-specific features such as price per square foot, transport accessibility score, and infrastructure indicators. Built a Random Forest classification model to identify Good vs Not Good property investments based on relative market pricing. Developed a Random Forest regression model to predict 5-year future property prices, achieving high R² and low RMSE. Used MLflow to track experiments, log metrics, and manage trained models. Deployed trained models using Streamlit, enabling real-time user input and predictions through a web interface. Achieved ~99% classification accuracy for rule-based investment labeling and R² ≈ 0.99 for future price prediction on historical data.
SQL & Relational Databases
IBM
June 1, 2026 – Present
Hackathon Participation Certificate
Unknown
June 1, 2026 – Present
Data Science Certification
Quality Thought
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects show a diverse application of data science skills across different domains (NLP, sales analytics, real estate). This breadth suggests adaptability and a willingness to tackle varied challenges, which can contribute positively to cultural fit in a dynamic team. The ongoing internship and multiple certifications also indicate a commitment to continuous learning and professional development. However, the experience level is entry-level, which might require more mentorship in a senior-level team.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach through multiple project implementations and an ongoing internship. The project descriptions indicate an ability to translate technical work into business value (e.g., 'Reduced document search time by 70%', 'Enabled management to track KPIs'). However, without direct assessment data on communication, logical reasoning, or teamwork, it's difficult to fully assess soft skills and operational fit beyond what's implied by project completion.