I bridge engineering physics and applied data science. Give me raw energy data — utility bills, weather feeds, battery telemetry — and I'll turn it into a pipeline, a model, or a decision.
My work sits at the intersection of three things: physical systems (how energy actually behaves), numerical methods (how to model that behavior precisely), and code (how to make it usable). I'm currently deepening my focus on energy engineering fundamentals while keeping data analytics as my core toolset.
| Domain | Tools |
|---|---|
| Core Language | Python |
| Data & Analytics | Pandas, NumPy, Jupyter Notebook |
| Scientific Computing | SciPy, SymPy (symbolic calculus & numerical discretization) |
| Workflow | Git, GitHub, automated data pipelines |
Renewable-Energy-Portfolio
Python tools for energy decisions — solar ROI from utility bills, EV battery degradation modeling, and automated weather pipelines for Morocco's solar market.
python data-analysis solar-energy battery-management
engineering-physics-lab
Symbolic-to-numerical physics engine for robotic arm kinematics — SymPy differentiation and NumPy finite differences to track trajectory, velocity error, and Coriolis effects.
python sympy numpy kinematics
Structured Query Language (SQL).
Open to internships and collaborations in energy engineering & data analytics.