Partner cross-functionally with data scientists, quantitative analysts, business analysts, software engineers, and project managers to manage the risk and uncertainty inherent in statistical and machine learning models in order to lead Capital One to the best decisions, not just avoid the worst ones.
Build and validate statistical and machine learning models through all phases of development, from design through training, evaluation and implementation
Develop new ways of identifying weak spots in model predictions earlier and with more confidence than the best available methods
Assess, challenge, and at times defend state-of-the-art decision-making systems to internal and regulatory partners
Leverage a broad stack of technologies Python, R, Conda, AWS, and more to reveal the insights hidden within huge volumes of data
Build upon your existing machine learning and statistical toolset - both by learning new technologies and by building custom software tools for data exploration, model performance evaluation, and more
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 translate the complexity of your work into tangible business goals, and challenge model developers to advance their modeling, data, and analytic capabilities
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.
Statistically-minded. You ve built models, validated them, and backtested 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:
Degree in statistics, math, engineering, economics, econometrics, financial engineering, finance,
or operations research with a quantitative emphasis preferred
Atleast 2 years relevant work experience
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)
Atleast 2 years of experience model development or validation
Atleast 2 years of experience in R or Python for large scale data analysis
Atleast 2 years of experience with relational databases and SQL
Strong analytical skills with high attention to detail and accuracy
Excellent written and verbal communication skills
Job Classification
Industry: BankingFunctional Area / Department: Data Science & Analytics, Role Category: Data Science & Machine LearningRole: Data Science & Machine Learning - OtherEmployement Type: Full time