MLOps Engineer with less than a year in Machine Learning & Data Science
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Yash Verma is an aspiring Data Scientist & MLOps Engineer with 8 months of internship experience. Currently pursuing an M.Sc. in Mathematics at IIT Delhi, he possesses strong skills in Python, SQL, MLflow, Docker, and various data science libraries. His project work demonstrates proficiency in building end-to-end MLOps pipelines, NLP text classification, and interactive data dashboards.
Indian Institute of Technology Delhi
M.Sc. in Mathematics · Mathematics
August 1, 2025 – Present
Maharshi Dayanand University
B.Sc. (Hons.) Mathematics · Mathematics
August 1, 2021 – June 30, 2024
Preet Public Sr. Sec. School
Class X · CBSE
N/A – May 31, 2019
Preet Public Sr. Sec. School
Class XII · CBSE
N/A – May 31, 2021
Disha Publication, Delhi
Subject Matter Expert Intern
June 1, 2024 – August 31, 2024
Delhi, Delhi, India
Quizzy, Remote
Subject Matter Expert Intern
May 1, 2024 – July 31, 2024
India
NioClass, Delhi
Subject Matter Expert Intern
September 1, 2022 – October 31, 2022
Delhi, Delhi, India
Cyber Intelligence Dashboard
June 1, 2026 – Present
Visualized breach intelligence using Dash/Plotly with 5+ advanced charts (Sunburst, etc.). Built a responsive dark-mode UI with real-time filtering by sector and country. Containerized with Docker and deployed to Hugging Face Spaces.
View ProjectSMS/Email Spam Classifier
June 1, 2026 – Present
Developed an NLP pipeline with NLTK preprocessing (tokenization, stopword removal, Porter Stemming) and TF-IDF vectorizer (3,000 features). Achieved 97% accuracy and 100% precision (zero false positives) via Multinomial Naive Bayes. Deployed a Streamlit app on Hugging Face Spaces for real-time spam detection.
View ProjectWine Quality MLOps
June 1, 2026 – Present
Architected a 5-stage MLOps pipeline with YAML config for reproducible workflows. Achieved 80.51% accuracy and 80.33% F1-score using SMOTE and Robust Scaling. Leveraged MLflow and DagsHub for remote tracking and model versioning. Deployed a Flask web interface for real-time predictions.
View ProjectData Science Salary Analysis
June 1, 2026 – Present
Cleaned and transformed the Kaggle DS Salaries dataset (~600 observations). Used Matplotlib and Seaborn to identify global salary trends across roles. Analyzed distributions and identified key growth drivers for data science careers.
Statistical Inference for PCA
June 1, 2026 – Present
Analyzed largest eigenvalue sampling behavior in PCA using financial return series. Built empirical distributions via 500 random samples to evaluate estimator stability. Applied t-tests and KS tests to validate normality and assess finite-sample bias.
Supervised Machine Learning - Regression, Classification, Ensemble Methods, Model Evaluation
Coursera (IBM)
June 1, 2026 – Present
Exploratory Data Analysis for ML - Anomaly Detection, Feature Engineering, Hypothesis Testing
Coursera (IBM)
June 1, 2026 – Present
Mathematics Quiz
YMCA University
January 1, 2023 – Present
Science Quiz
D.A.V. Centenary College
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in practical applications of machine learning and data science, aligning well with an MLOps role. The diversity of projects (MLOps pipeline, NLP classifier, dashboard, statistical inference) shows a broad technical curiosity. Their academic background and ongoing Master's degree indicate a commitment to continuous learning. However, the absence of team-based project experience or contributions to open-source projects makes it difficult to fully assess cultural fit in a collaborative engineering environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work independently on complex technical tasks. Their role as a PG Placement Coordinator suggests organizational and communication skills, which are beneficial for team collaboration and project management. However, the lack of professional software engineering experience means operational fit in a fast-paced MLOps environment would need further validation.