Machine Learning Engineer and Business Scientist focused on applied AI, analytics, quantitative thinking, and product-driven execution. Strong overlap across machine learning, finance, optimization, and systems design.
Applied technical work with business relevance.
This portfolio highlights a blend of machine learning engineering, business analytics, financial reasoning, and technical problem solving. The profile is built for roles and projects that need both quantitative depth and practical execution.
Technical, analytical, and domain depth.
Model development, feature engineering, evaluation, ML pipelines, practical deployment thinking, and agentic AI workflows.
EDA, hypothesis testing, experimentation, probability, regression thinking, and business metric design.
Corporate finance concepts, derivatives thinking, business modeling, trade-off analysis, and quantitative decision support.
Selected build directions and applied problem-solving tracks.
A structured options strategy system focused on payoff logic, break-even calculation, scenario analysis, and strategy comparison for different market views.
An agentic workflow concept for collecting company information, parsing financial structures, and producing detailed analysis outputs in a single report-oriented interface.
Pipeline-oriented thinking for spare parts intelligence, structured processing, and operational data handling for business use cases.
Analytics-led work around experimentation, KPI design, decision support, and measurable business impact.
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