I’m a data professional growing toward Data Scientist and Machine Learning roles, with a practical foundation built through 4+ years of solving real business problems.
My professional experience began in analytics: working with stakeholders, shaping ambiguous questions into measurable problems, preparing imperfect data, and communicating results clearly. That foundation now informs how I approach machine learning. A useful model is not only accurate—it must be understandable, reproducible, and connected to a decision someone needs to make.
I build hands-on projects across classical machine learning, deep learning, and natural language processing. My work includes end-to-end prediction workflows, feature engineering, model comparison and evaluation, sentiment classification, sequence models with RNNs, LSTMs and GRUs, and small applications that make model outputs easier to explore.
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.
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 looking for Data Scientist, Machine Learning, and Deep Learning opportunities where I can contribute my production-minded analytics experience while growing deeper in modeling and AI systems. I’m particularly drawn to teams that value experimentation, sound evaluation, collaboration, and continuous learning.