MS in Data Science • Machine Learning • MLOps • Analytics

I turn data into decisions with statistical modeling, machine learning, and production-grade data systems. I enjoy building end-to-end solutions—from data pipelines and feature engineering to model deployment and monitoring.

• Currently: Master’s in Data Science
• Interests: ML, NLP, CV, Time Series, Recommenders, MLOps
• Open to: Collaborations on applied ML/data engineering
• Portfolio: Browse all public projects (auto-sorted by latest updates)
  • https://github.com/mdtanvir-hasan?tab=repositories&sort=updated

Portfolio • Resume • LinkedIn • Email • Kaggle • Hugging Face

• Languages: Python, R, SQL
• ML/DL: scikit-learn, PyTorch, TensorFlow
• Data: Pandas, NumPy, Polars
• Visualization: Matplotlib, Seaborn, Plotly
• MLOps / Data Eng: Docker, Kubernetes, Airflow, FastAPI, GitHub Actions
• Cloud: AWS/GCP (basics)

• End-to-end ML: problem framing → data pipelines → feature engineering → model training/validation → deployment → monitoring.
• AI engineering: build and ship LLM/NLP/CV systems (retrieval, prompt engineering, fine-tuning, evaluation, guardrails).
• MLOps: reproducible experiments, CI/CD for models, model registries, containers, orchestration, and observability.

• Supervised/unsupervised learning, time-series forecasting, recommendation systems.
• NLP: embeddings, RAG, instruction-tuning, evaluation (BLEU/ROUGE/BERTScore), safety.
• CV: transfer learning, data augmentation, efficient inference.
• Data engineering: ETL/ELT, dbt-style transformations, warehouse/lakehouse patterns.
• Serving: batch/online inference, REST/gRPC, async pipelines.

• Pin your top projects on your GitHub profile to feature them here prominently.
• Tip: Use descriptive READMEs with problem, approach, results, and reproducibility steps.

These sections update automatically as you push code, open PRs, or create repos.

• Activity graph (contributions over time)

  

• Snapshot metrics (recent activity, languages, follow-up)

  

• Stats and languages

  

  

• MS in Data Science — Your University, YYYY–Present
• BS in Your Major — Your University, YYYY–YYYY

• Example: AWS Certified Machine Learning – Specialty (Year)
• Example: Google Professional Data Engineer (Year)
• Example: TensorFlow Developer Certificate (Year)

• Paper: Title — Venue, Year
• Talk: Title — Conference/Meetup, Year
• Blog: Post Title — Platform, Year

• Kaggle: https://www.kaggle.com/YOUR_KAGGLE_USERNAME
• Hugging Face: https://huggingface.co/YOUR_HF_USERNAME
• Google Scholar: https://scholar.google.com/citations?user=YOUR_SCHOLAR_ID
• Medium/Substack: LINK_TO_BLOG

• All public repositories (auto-updated): https://github.com/mdtanvir-hasan?tab=repositories&sort=updated
• By topic:
  • Data Science: https://github.com/search?q=user%3Amdtanvir-hasan+topic%3Adata-science&type=repositories&s=updated
  • Machine Learning: https://github.com/search?q=user%3Amdtanvir-hasan+topic%3Amachine-learning&type=repositories&s=updated
  • MLOps: https://github.com/search?q=user%3Amdtanvir-hasan+topic%3Amlops&type=repositories&s=updated

• GitHub: https://github.com/mdtanvir-hasan
• LinkedIn: https://www.linkedin.com/in/YOUR_LINKEDIN_SLUG/
• Email: YOUR_EMAIL@example.com
• Calendly (optional): https://calendly.com/YOUR_HANDLE/intro-chat