Software Engineer with less than a year in ML, NLP & CRM
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Computer Science graduate with demonstrated expertise across the full data science lifecycle - from EDA and feature engineering to model training, NLP pipelines, ensemble methods, and Streamlit deployment - combined with hands-on Salesforce CRM development (Apex, LWC, SOQL, Flows, Admin). Deployed 3 production-grade ML applications and built BI dashboards using Tableau and Power BI. Solved 150+ problems on LeetCode with a 100-day streak, demonstrating strong algorithmic and problem-solving foundations. Eager to contribute to data-driven or CRM-focused engineering teams at scale.
Cuvette Academy
Program · Data Science
July 1, 2025 – May 1, 2026
Geeta Engineering College, Kurukshetra University
B.Tech · Computer Science & Engineering
August 1, 2020 – June 30, 2024
U.T College, Begusarai
Class 12 (PCM)
N/A – Present
B.R DAV Public School, Begusarai
Class 10
N/A – Present
Cloud Analogy
Salesforce Developer Trainee
February 1, 2024 – July 1, 2024
India
IPL Win Probability Predictor
June 1, 2023 – June 1, 2024
Built a real-time match-state prediction system using a Scikit-learn Logistic Regression pipeline trained on multiseason IPL match and ball-by-ball delivery data. Engineered 16 match-state features - CRR, RRR, runs remaining, balls remaining, wickets in hand, batsman strike rate, economy rate, venue, toss decision, season - improving model signal significantly over raw stats. Resolved data quality issues: historical team name inconsistencies (Delhi Daredevils -> Delhi Capitals), D/L match exclusions, and season-year extraction; serialized 5 Pickle artifacts for sub-second inference latency.
View ProjectSMS Spam Detection App
June 1, 2023 – June 1, 2024
Built end-to-end NLP classification pipeline achieving 97%+ accuracy & 100% precision on imbalanced SMS data using Random Forest + TF-IDF. Applied full NLP preprocessing (tokenization -> stopword removal -> Porter stemming -> TF-IDF); benchmarked 11 ML models(Naive Bayes, Logistic Regression, SVM, KNN, Decision Tree, Random Forest, XGBoost, AdaBoost, Bagging, Extra Trees, Gradient Boosting), selected Random Forest by precision on imbalanced spam class. Conducted EDA (word clouds, frequency plots, heatmaps); deployed live app on Streamlit Cloud with pickle-serialized model for real-time inference.
View ProjectBook Recommendation System
June 1, 2023 – June 1, 2024
Engineered a dual-mode recommendation engine - Popularity-Based filtering (top-50 books, 250+ ratings threshold) and Collaborative Filtering using user-item pivot matrices with cosine similarity for personalised recommendations. Processed and merged 3 datasets (270,000+ books, ratings, users); handled null values, duplicate ISBNs, type mismatches, and a 196->50 row deduplication artifact post-merge, ensuring clean model input. Filtered to power users (200+ ratings) and books rated by 50+ users; serialized 4 Pickle artifacts achieving sub-2second recommendation latency on Streamlit Cloud.
View ProjectFIFA Player Analytics Dashboard
June 1, 2023 – June 1, 2024
Built an interactive Tableau dashboard analysing FIFA player statistics across positions, nationalities, and clubs using LOD Expressions (FIXED, INCLUDE), calculated fields, and drill-down filters. Designed parameter controls and dashboard actions enabling dynamic cross-filtering to surface player value distribution, skill rating patterns, and club performance benchmarks.
Cultural Fit Analysis
The candidate demonstrates a strong drive for continuous learning and skill development, as seen by pursuing a Data Science program after a B.Tech and actively solving LeetCode problems. The diverse project portfolio, spanning ML, NLP, and BI, indicates a broad interest in technology and a proactive approach to gaining practical experience. The Salesforce Developer Trainee role, combined with personal data science projects, shows a versatile skill set that could fit well into teams requiring cross-functional capabilities or those valuing individuals who can bridge different technical domains. The focus on practical application and deployment in projects aligns with a results-oriented culture.
Soft Skills & Operational Fit
The candidate's participation in national-level football suggests discipline, teamwork, and performance under pressure. The consistent LeetCode streak also indicates dedication and perseverance. The project descriptions are clear and detailed, implying good communication of technical concepts. The blend of Data Science and Salesforce skills suggests adaptability and a willingness to learn diverse technologies, which is beneficial for operational fit in dynamic environments.