Vikas Kini

Washington, DC | kinimv@gmail.com | LinkedIn

Experience

I am a Senior Associate, Data Science at Capital One. I'm deeply passionate about leveraging data to solve complex and intriguing problems. My journey in data science is driven by a relentless curiosity and a commitment to uncovering insights that can transform industries and safeguard financial systems.

At Capital One, I specialize in Risk Management, where I develop sophisticated machine learning models to detect and prevent money laundering activities. This role involves not only the technical aspects of feature engineering, model training, and evaluation but also a strong focus on compliance and communication with regulators and auditors. One of my key projects includes the development of a transformer-based approach to detect money laundering in banking transactions, which has significantly enhanced our monitoring capabilities.

Throughout my career, I have built and deployed various types of machine learning models, including XGBoost and Logistic Regression, tailored to monitor transaction activities in a vast pool of over $250 billion in consumer deposits. I lead the model monitoring efforts, ensuring the accuracy and reliability of our risk scoring models through continuous evaluation and retraining.

Previously, as a Data Scientist Intern at Hach Company, I developed predictive models for municipal water systems, incorporating factors like chlorine levels, seasonality, and temperature. My models helped forecast adverse events, providing valuable insights for city-scale water management.

During my tenure at Schneider Electric, I streamlined data processes by designing a SQL database that improved data retrieval efficiency, saving significant time and resources. Additionally, my consultancy experience at Kini Computational Science allowed me to apply machine learning techniques across various industries, from revenue forecasting to collaborative filtering for user recommendations.

My work is not just about building models; it's about creating solutions that address real-world challenges. Whether it's enhancing financial security or optimizing water systems, my goal is to use data science to make a meaningful impact.

Capital One

Hach Company

Schneider Electric

Education

I hold a Master's degree in Information Management with a specialization in Data Science, and a Bachelor's degree in Applied Physics with a minor in Applied Mathematics from the University of Washington. My academic background has equipped me with a strong foundation in data analysis, machine learning, and computational modeling. My applied physics degree, in particular, has honed my analytical problem-solving skills, enabling me to approach complex data challenges with precision and creativity.

My love of poker sparked my curiosity in data science. The strategic thinking and probabilistic reasoning required in poker naturally transitioned into an interest in data science, where I could apply similar principles to analyze data and build predictive models.

University of Washington

Computing Skills

My technical skills include Python (NumPy, pandas, SciPy, PyTorch, scikit-learn, Matplotlib, Jupyter, seaborn), Spark, R, and SQL. I am experienced in building, training, and evaluating machine learning models, as well as conducting data analysis and visualization.