Data Scientist & Analytics Engineer | Machine Learning · Deep Learning · Advanced SQL · Power BI
I build predictive machine learning systems, statistical models, and high-performance data analytics architectures. My work spans deep learning architectures from scratch, end-to-end production ML pipelines (FastAPI, MLflow, threshold tuning), advanced SQL cohort models, and dimensional data modeling with Star Schema and DAX.
Currently pursuing a Minor in Artificial Intelligence & Data Science at IIT Mandi, with an active focus on scalable machine learning systems and Agentic AI workflows.
| Domain | Tools & Technologies |
|---|---|
| Machine Learning & Deep Learning | PyTorch, Scikit-Learn, LightGBM, XGBoost, SHAP, MLflow |
| Programming & API Frameworks | Python, SQL, FastAPI, Pydantic, Bash |
| Data Analysis & Processing | Pandas, NumPy, Power Query (M), Data Cleaning & Imbalance Handling (SMOTE) |
| Databases & Data Modeling | PostgreSQL, Dimensional Modeling (Star Schema), Window Functions |
| Business Intelligence & Dashboards | Power BI, DAX, Power Pivot (xVelocity Engine), Microsoft Excel |
| Developer Tools & Practices | Git, GitHub, VS Code, DBeaver, Automated Testing (Pytest) |
Tech Stack: Python, LightGBM, XGBoost, MLflow, FastAPI, Pydantic, SHAP
- Developed an end-to-end ML pipeline with custom class-imbalance strategies (SMOTE, cost-sensitive weighting, undersampling).
- Optimized classification decision thresholds directly against financial cost matrices to maximize net fraud recovery.
- Built explainability modules using SHAP and tracked iterative model runs via MLflow.
- Deployed a production-ready real-time scoring service with a validated FastAPI schema and automated unit tests.
🔗 Repository: github.com/kra10m/fraud-detection (or subfolder in your ML repo)
Tech Stack: Python, NumPy, PyTorch, Matplotlib
- Implemented vectorized multi-layer Fully Connected Neural Networks (FCNN) from scratch using pure NumPy.
- Conducted diagnostic research on gradient propagation, vanishing gradients, and learned spatial representations.
- Benchmarked architectures across MNIST and Tiny ImageNet with PyTorch, running systematic ablation studies on depth and initialization.
🔗 Repository: github.com/kra10m/fully-connected-neural-networks (or subfolder in your ML repo)
Tech Stack: PostgreSQL, Advanced SQL, Window Functions
- Processed and analyzed 100,000+ retail transaction records using high-performance analytical SQL queries.
- Built dynamic behavioral customer segments to evaluate retention curves and lifetime values.
- Engineered cohort-based revenue tracking pipelines utilizing window operations (
LAG,LEAD, moving averages).
🔗 Repository: github.com/kra10m/SQL_Projects_Data_Analytics
Tech Stack: Microsoft Excel, Power Query (M), Power Pivot, DAX, Star Schema
- Engineered an enterprise-grade reporting solution eliminating row-level formula bloat via the xVelocity/VertiPaq in-memory engine.
- Designed a normalized Star Schema data model (
1:*cardinality) linking fact tables across customer, product, and dynamic calendar dimensions. - Developed defensive DAX measures for Year-over-Year (YoY) revenue changes, profit distributions, and basket sizes (AOV).
🔗 Repository: github.com/kra10m/Ecommerce_Excel_Portfolio
For broader exploratory work, specialized queries, and modular components, explore these dedicated collections:
- Machine Learning & Data Science Portfolio — Deep learning benchmarks, predictive modeling pipelines, and production APIs.
- SQL Analytics Repository — Complex queries, window functions, and database schema architectures.
- Python Data Analytics Repository — Exploratory data analysis, scraping pipelines, and statistical analyses.
- Power BI Business Intelligence — Multi-source dashboards, DAX measures, and stakeholder KPIs.
June 2024 – December 2024
- Built Power BI dashboards and automated reporting workflows for educational operations and revenue tracking.
- Analyzed student enrollment, fee collection metrics, and operational datasets to guide administrative choices.
- Monitored KPI pipelines to maintain consistent data hygiene across internal dashboards.
- Minor in Artificial Intelligence & Data Science — IIT Mandi (2025 – Present)
- Bachelor of Computer Applications (BCA) — LN Mishra Institute (2021 – 2024)
- 📧 Email: atandon027@gmail.com
- 💼 LinkedIn: linkedin.com/in/abhinav-tandon-212a54388
- 🐙 GitHub: github.com/kra10m