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Data Analyst with less than a year in Python, SQL, Power BI, and Machine Learning.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Entry-level Data Analyst with a postgraduate background in Statistics and hands-on experience in Python, SQL, Power BI, and Excel. Skilled in data analysis, machine learning, and dashboard development, with the ability to translate data into actionable insights. Developed a startup success prediction model using XGBoost (87% accuracy), conducted sentiment analysis using FinBERT and ROBERTa, and built interactive dashboards to analyze business performance. Strong foundation in statistical analysis and problem-solving, with a focus on data-driven decision-making
MAHATMA GANDHI UNIVERSITY KOTTAYAM
Master of Science · Statistics With Data Science
June 1, 2024 – June 1, 2026
KANNUR UNIVERSITY
Bachelor of Science · Statistics
June 1, 2021 – March 1, 2024
ICT Academy of Kerala
ML/AI Intern
February 1, 2026 – March 1, 2026
India
Startup Success Prediction & Deployment
June 1, 2026 – Present
Developed a machine learning model using XGBoost to predict startup success (87% accuracy) Processed and engineered features from real-world startup data, including handling class imbalance using SMOTE Optimized model performance through hyperparameter tuning and evaluation techniques Designed a hybrid rule-based + ML system to improve prediction interpretability Built and deployed a Flask-based web application delivering real-time predictions and decision-support insights
NLP Sentiment Analysis (FinBERT vs RoBERTa)
June 1, 2026 – Present
Conducted comparative analysis of FinBERT and Twitter RoBERTa for sentiment classification Evaluated model performance across financial and social media text datasets (80% accuracy each) Analyzed differences in inference speed and domain-specific performance Identified model strengths: FinBERT for financial text, RoBERTa for social media data Proposed improvement strategies including ensemble modeling and domain-based model selection
Sales Analysis Dashboard (Power BI & Excel)
June 1, 2026 – Present
Developed interactive dashboards using Power BI to analyze revenue and sales performance Identified key trends and regional performance gaps to support business insights Automated data transformation using Power Query to improve reporting efficiency
Demystifying Generative AI for Leaders
Infosys springboard
June 1, 2026 – Present
R for Data Science: Data Visualization
Infosys springboard
June 1, 2026 – Present
Beginner Statistics for Data Analytics-Learn the Easy Way
Udemy
June 1, 2026 – Present
Statistics for Business Analytics and Data Science A-Z™
Udemy
June 1, 2026 – Present
Master SQL For Data Science
Unknown
June 1, 2026 – Present
Deloitte Australia – Data Analytics Job Simulation
Unknown
June 1, 2026 – Present
Tata - GenAI Powered Data Analytics Job Simulation
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Statistics and ongoing Master's in Statistics with Data Science, combined with diverse project work (startup prediction, NLP, sales dashboards), indicates a strong interest in data-driven problem-solving. The variety of certifications, including those in Generative AI and R for Data Science, suggests a continuous learning mindset. The projects align well with a Data Analyst role, showing a breadth of skills from traditional BI to machine learning. However, the lack of team-based project descriptions or collaborative work outside of the internship limits the assessment of cultural fit in a larger team environment.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning through numerous certifications. Project descriptions indicate an ability to translate data into actionable insights and present findings. The internship experience suggests an ability to work within a project lifecycle, from development to deployment and presentation. However, the limited professional experience (one short internship) means operational fit in a senior role is largely unproven.