Tinotenda Mhlanga

I am a senior data scientist specializing in fraud analytics, AML modeling, and machine learning, with hands-on experience in large-scale transactional environments. I combine strong academic credentials in finance and computer science with practical skills in SQL, Python, and C++ to deliver measurable business impact.

At Discover Financial Services I identified a critical false positive in fraud reason codes and led analyses that materially reduced erroneous losses. I designed analytical tools and complex SQL pipelines in Snowflake to enrich transactional insights and support strategic risk decisions.

I improved AML watchlist processing performance dramatically by implementing multithreading in Python (reducing parsing and pairing runtimes by 37% and 77%, respectively) and updated an open-source C++ package to meet AML requirements. I also built machine-learning models for computer vision and crisis detection during a data science internship supporting Horizon 2020 smart-city initiatives.

I hold a MS in Finance (Financial Technology) and a MS and BS in Computer Science, and I look to apply my blend of data engineering, ML modeling, and domain expertise to high-impact roles that value rigorous analytics, reliable production systems, and measurable operational improvements.