Skills

Machine Learning: Practical application of supervised and unsupervised learning models, including Linear Regression, K-Nearest Neighbors (KNN) for classification, and clustering algorithms.
Data Analysis & Inference: Proficient in the full data lifecycle, from exploratory data analysis (EDA) and data cleaning to hypothesis testing and A/B testing for statistical inference.
Algorithms & Data Structures: Strong foundation in core computer science principles, including sorting, searching, recursion, and the implementation of essential data structures like linked lists, trees, and hash tables.
Foundational Mathematics: Deep understanding of the mathematical principles underpinning data science, including Linear Algebra, Multivariable Calculus, and Probability theory.

Leadership & Project Management: Experienced in leading teams to complete large-scale projects, with a focus on efficient resource management, clear delegation, and meeting long-term deadlines.
Analytical Problem-Solving: A dual approach to problem-solving that combines creative, out-of-the-box thinking with straightforward, algorithmic analysis to find the most effective solutions.
Communication & Collaboration: Articulate in both public speaking and written communication, with strong interpersonal skills that facilitate seamless work in team environments or independently.
Initiative & Adaptability: A dependable and curious self-starter, driven to learn new skills and adapt quickly to evolving project demands and technologies.

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