As a Data Scientist at Capital One, you will 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.

At Capital One India, we are at the cutting edge of solving some of the fundamental business problems using advanced data methodologies, statistics, and machine learning algorithms.

Team Description

The DataLabs Anti-Money Laundering (AML) team is on a journey to modernize the way Capital One identifies potential money laundering, terrorist financing, and human trafficking using advanced analytic techniques, statistics, and machine learning models. Our team develops data sourcing, predictive models, monitoring, and reporting using tools such as AWS, Snowflake, Python, and Spark. As the model development team for advancing transaction monitoring with machine learning, our team is responsible for end-to-end development, deployment, and monitoring of these critical risk management models.

Our mission is to empower data-driven decision-making, foster innovation without limits, and make Capital One's AML program pre-eminent. We achieve this by implementing rigorous data governance practices, constructing analytical data stores, and harnessing the power of analytics to unlock valuable insights.

About the Role

Partner cross-functionally with teams throughout the enterprise including Product and Tech to enable capturing risky behavior. You will enhance your technical and analytical skills while also working with leaders to influence business strategies.
Collaborate with Quants, Data Scientists, Data Analysts, Business Analysts, and risk managers to drive innovation, shape strategic initiatives and propel AML Transaction monitoring to new heights in the data management landscape.
Utilize data to drive real-world impact and thrive in a collaborative, forward-thinking environment.
Assess, challenge, and sometimes defend state-of-the-art decision-making systems to internal and regulatory partners.
Build upon your existing machine learning and statistical toolset by learning new technologies and by building custom software tools for data exploration, model performance evaluation, and more.
Use Open Source/Digital technologies to mine complex, voluminous, and different varieties of data sources and platforms.
Build data products, data solutions, tools, and capabilities to enable self-service frameworks for data consumers.
Demonstrate ability to explore and quickly grasp new technologies to progress varied initiatives.
Drive analysis that provides meaningful insights on business strategies.
Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management.
Flex your interpersonal skills to defend the model with validators, auditors, and regulators.

The ideal candidate is:

Inquisitive: 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.
ML/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, time series, and deep learning.
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.

Basic Qualifications:

Bachelor's degree in statistics, math, engineering, economics, econometrics, financial engineering, finance, or operations research with a quantitative focus.
At least 4 years relevant work experience in data analytics.
At least 3 years of experience in Python or R.

Preferred Skills:

Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, panel data models, decision trees, machine learning methods).
4 years of experience model development or validation.
4 years of experience in R or Python for large-scale data analysis.
4 years of experience with relational databases and SQL.
Strong analytical skills with high attention to detail and accuracy.
Excellent written and verbal communication skills.
Experience in the financial services industry.
Experience in Fraud Anti-money Laundering domain

Principal Data Scientist at Capital One

Job Description

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