Senior Associate, Data Scientist - Audit Data Science | Richmond, VA, USA

Senior Associate, Data Scientist - Audit Data Science Senior Associate, Data Scientist - Audit Data Science Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. Innovation is at the heart of everything we do on the Audit Analytics and Innovation team. We're not a traditional Data Science team: we build creative ML solutions across multiple domains, such as LLM based chatbots, GenAI powered applications, AML/Fraud identification, and Customer call transcripts intelligence. Opportunities to learn and build fast allow our team members to develop towards their full potential. We partner closely with product, tech, and design teams to enable faster build-to-market cycles for product features that delight our customers with dynamic and integrated experiences. You will be the driving force to experiment, innovate, and create next-generation features powered by the latest emerging NLP and Generative AI technologies. If you love a fast-paced, highly rewarding environment, and you love being a builder and communicator, this is the place for you. In this role, you will: • Partner with a cross-functional team of data scientists, data analysts, risk professionals, software engineers, and product managers to manage risk and uncertainty in order to lead Capital One to the best decisions, not just avoid the worst ones • Leverage a broad stack of technologies (Python, Conda, Flask, Dash, Hugging Face, LangChain, AWS, H2O, Spark, and more) to reveal the insights hidden within huge volumes of numeric and textual data • Build machine learning and NLP models through all phases of development, from design through training, evaluation, validation, and implementation. • Productionize highly scalable data pipelines for faster feature changes and updates, and implementing data validation framework and quality tests • Develop visualization tools to streamline model monitoring reports • Investigate new technology to advance data management, model development and deployment and drive change in enterprise ML products • Flex your interpersonal skills to translate the complexity of your work into tangible business goals The Ideal Candidate is: • Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. • Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea. • Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. • Collaboration and Communication. You're capable of effectively articulating data insights and analytics strategies to a diverse audience, including auditors, engineers, product managers and leadership. • Statistically-minded. You've built models, validated them, and back tested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning. • A data guru. "Big data" doesn't faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.