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Data Science with less than a year in AI & Data Analytics
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Assessing your cultural and operational fit
Analytical graduate in AI & Data Science seeking entry-level roles in Data Analytics, Data Science, and AI. Skilled in Python, SQL, MS Excel, and end-to-end data analysis from raw data cleaning and ML modelling to BI dashboard delivery. Proven ability to build scalable AI pipelines, agentic LLM systems, and business intelligence tools that convert complex datasets into clear, actionable decisions.
Muthayammal Engineering College
B.Tech · Artificial Intelligence & Data Science
August 1, 2022 – June 30, 2026
Cntxt.ai
Data Annotator Intern
March 1, 2026 – April 1, 2026
India
Elevate Labs
Data Science Intern
August 1, 2025 – September 1, 2025
India
Edunet Foundation
AI Intern
January 1, 2025 – February 1, 2025
India
Financial Document Analyzer
January 1, 2026 – Present
Problem Solution: Manual financial PDF review is slow and inconsistent. Built a multi-agent AI pipeline where specialised agents handle parsing, entity extraction, and summarisation in sequence – reducing manual review time by ~60%. Engineering: Async Celery + Redis job queue handles concurrent document requests without API blocking; SQLite persists every job's lifecycle state for full auditability and result retrieval.
View ProjectReal-Time Stock Market Analytics Dashboard
January 1, 2026 – Present
Problem Solution: Raw tick data gives no visibility into risk trends. Computed daily returns and 30-day rolling volatility for 10+ live tickers; built a Streamlit dashboard with dynamic ticker filtering and real-time refresh for non-technical users. Impact: Transformed raw market data into an at-a-glance risk and return view, cutting manual metric calculation to zero.
View ProjectOncoPredAI - Breast Cancer Recurrence Prediction
January 1, 2026 – Present
Problem → Solution: Class imbalance causes models to ignore the minority (recurrence) class - the most clinically critical outcome. Applied class-weight tuning and 5-fold CV across LR, Decision Tree, and Random Forest; achieved 92% accuracy and 0.89 F1 with strong minority-class recall.
View ProjectDitto AI Caller - Aarya
January 1, 2026 – Present
Problem → Solution: Insurance sales lose 22-30% prospects to no-shows. Built "Aarya" – an AI voice agent automating pre-call reminders and post-call follow-ups, targeting <15% no-show rate vs a 22-30% baseline. Engineering: TRAI-compliant 9AM-9PM windows, WhatsApp fallback on no-answer, FastAPI + Plivo telephony backend – zero human intervention per call.
View ProjectIndia Crop Yield Predictor
January 1, 2026 – Present
Problem → Solution: Crop failures go undetected until harvest – too late to act. Built an end-to-end ML pipeline predicting district-level yield across 35 Indian states at 91% R², flagging at-risk districts 4-6 months before harvest on 246K rows (1997-2015). Engineering: LightGBM + Optuna tuning, SHAP explainability, MLflow tracking, FastAPI REST endpoint, Docker deployment, Streamlit choropleth dashboard with Evidently AI drift monitoring.
View ProjectMcKinsey Forward Program (Strategy & Problem-Solving)
McKinsey
October 1, 2025 – Present
Google Data Analytics Professional
January 1, 2024 – Present
Microsoft Power BI Data Analyst
Coursera
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a diverse range of applications for data science, from crop yield prediction and financial document analysis to breast cancer recurrence and AI voice agents. This breadth of interest and application suggests adaptability and a willingness to tackle varied challenges, which can contribute positively to cultural fit. The academic projects are well-described with clear problem statements and engineering details, indicating a structured approach. The certifications from Google and Coursera, along with the McKinsey program, show a proactive attitude towards continuous learning and professional development. However, the candidate's experience level is entry-level, which might require mentorship and integration into a senior team's culture.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving skills by identifying real-world problems and proposing AI/ML-based solutions in their projects. Their experience in developing and maintaining annotation consistency guidelines suggests an ability to work collaboratively and ensure data quality. The McKinsey Forward Program indicates an interest in strategy and problem-solving, which are valuable for operational fit. However, the short duration of internships and academic nature of projects mean real-world operational experience in a fast-paced, senior-level environment is limited.