Data Scientist

Qualifications

Bachelor’s degree in a quantitative field such as Statistics, Computer Science, Engineering, or Applied Mathematics with 0–2 years of relevant work experience

Responsibilities

• Predictive Modeling: Lead predictive analytics projects using large, complex data sources to develop scalable machine learning and statistical models for business banking insights.
• Data Acquisition: Acquire, consolidate, and preprocess data from various structured and unstructured sources to enable high-quality analytics outcomes.
• Feature Engineering: Design and implement methods for extracting relevant features from big data sets to improve model accuracy and performance.
• Model Development & Validation: Build, test, and maintain predictive models; validate model results to ensure robustness, compliance, and business relevance.
• Data Interpretation: Analyze data trends and patterns to answer complex business questions and drive actionable recommendations.
• Business Collaboration: Work closely with cross-functional stakeholders including product, risk, technology, and analytics teams to align models with business goals.
• Insight Presentation: Present analysis and insights clearly and concisely using data visualization tools such as Tableau to enable data-driven decision-making.
• Model Governance: Ensure all modeling practices align with internal model risk management standards and regulatory expectations in financial institutions.
• Performance Monitoring: Measure and monitor the business impact of applied models and continuously improve based on feedback and outcomes.
• Advanced Statistical Techniques: Use advanced analytical tools and programming languages (Python, R, SAS) to perform complex statistical modeling and analysis.
• Stakeholder Communication: Communicate technical results to non-technical stakeholders through effective storytelling and data summaries.
• Compliance & Standards: Ensure all data use and model deployment is compliant with U.S. Bank’s internal policies and regulatory standards.

Skills and Qualifications

• Strong analytical skills and independent problem-solving ability
• Expertise in data extraction, preparation, and interpretation using Python, R, SAS, and SQL
• Deep knowledge of machine learning techniques, predictive modeling, and statistical methods
• Proficiency in working with big data and implementing scalable analytics solutions
• Familiarity with the financial services domain, especially business and corporate banking
• Experience managing multiple stakeholders in a matrixed organization
• Proficient in data visualization and reporting using tools like Tableau
• Strong project management and interpersonal communication skills
• Understanding of data governance frameworks and model risk compliance

Certifications (Preferred)

• Google Data Analytics, Microsoft Certified: Azure Data Scientist Associate, SAS Certified Data Scientist, or equivalent