I’m a data professional with a strong interest in Data Science and AI roles, with 4+ years of experience across analytics, predictive modeling, and practical problem-solving in business environments.
Over the course of my career, I’ve worked in analytics roles that gradually expanded into deeper data science work—starting with hands-on analysis, stakeholder collaboration, and data preparation, and evolving toward modeling, experimentation, and machine learning solutions. That progression has shaped how I approach problems: not just finding answers, but building reliable, business-relevant solutions that can be explained and trusted.
I build hands-on projects across classical machine learning, deep learning, natural language processing, and emerging Generative AI applications.
What I bring
Problem-solving
I frame questions as testable hypotheses, explore data carefully, establish sensible baselines, and evaluate results with metrics that fit the problem.
ML & deep learning
I use Python, scikit-learn, TensorFlow and Keras for predictive modeling, NLP, neural networks, and sequence-based learning projects.
Strong data foundations
SQL, data preparation, analytics engineering, Azure, Databricks and Microsoft Fabric help me work beyond the notebook and understand the full data lifecycle.
Business communication
My analytics background helps me explain technical findings, collaborate with stakeholders, and connect model performance to practical value.
Data storytelling
I turn complex analysis into clear insights and actionable recommendations, helping teams make better decisions with confidence.
End-to-end delivery
I enjoy working across the full workflow—from data preparation and analysis to model development, evaluation, and presentation.
Curious and growth-driven
I’m motivated by continuous learning, new tools, and solving problems where data can create measurable impact.
Reliable and detail-oriented
I value clean workflows, careful validation, and thoughtful execution so that insights are not only useful but dependable.
How I work
I prefer disciplined, end-to-end work: understand the objective, inspect the data, build a baseline, iterate with evidence, document trade-offs, and present the result in plain language. I am especially interested in applied ML and DL problems where careful data work matters as much as the model architecture.
What I’m looking for
I’m seeking opportunities in Data Science, Machine Learning, Deep Learning, and Generative AI, where I can apply my experience to real-world AI solutions and keep building my expertise in creating intelligent systems.