Risk Management - Data Scientist - Senior Associate

Location: Columbus, OH, United States

Job Family: Predictive Science

About the Role

At JP Morgan Chase, we are at the forefront of financial innovation, and our Risk Management team plays a pivotal role in safeguarding our global operations. As a Senior Associate Data Scientist in Predictive Science, based in Columbus, OH, you will join a dynamic group focused on harnessing cross-line-of-business data to drive strategic credit decisions. This role involves applying advanced analytics to uncover hidden patterns in vast datasets spanning consumer banking, investment management, and commercial lending, ultimately informing robust credit strategies that mitigate risks and enhance profitability in a complex regulatory landscape. Your day-to-day will center on developing sophisticated predictive models using machine learning and statistical techniques to assess creditworthiness, forecast defaults, and optimize portfolio performance. Collaborating with cross-functional teams including risk analysts, data engineers, and business leaders, you will translate complex data insights into actionable recommendations that align with JP Morgan Chase's commitment to responsible banking. You will also ensure models adhere to stringent internal and external standards, such as those set by the Federal Reserve and OCC, while leveraging cutting-edge tools to handle petabyte-scale data from diverse sources. This position offers an opportunity to contribute to high-impact projects that shape the future of financial risk management at one of the world's largest banks. With access to unparalleled resources and mentorship from industry experts, you will grow your expertise in a supportive environment that values innovation and integrity. If you are passionate about using data science to solve real-world challenges in finance, join us in Columbus to make a meaningful difference.

Key Responsibilities

• Leverage cross-line-of-business data from JP Morgan Chase's vast ecosystem to uncover actionable insights for credit risk strategies
• Develop and deploy machine learning models to predict credit default risks and optimize lending decisions
• Collaborate with risk management teams to integrate data-driven recommendations into business processes
• Analyze large datasets using statistical methods to identify patterns in customer behavior and market trends
• Design and implement predictive models for fraud detection and portfolio stress testing
• Communicate findings through visualizations and reports to senior leadership and regulatory bodies
• Stay abreast of emerging technologies and regulatory changes impacting financial risk management
• Mentor junior data scientists and contribute to the firm's data science best practices
• Ensure model accuracy, interpretability, and compliance with internal governance standards
• Participate in cross-functional projects to enhance data infrastructure for scalable analytics

Required Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
• Master's degree or PhD in a quantitative discipline preferred
• 3+ years of experience in data science, machine learning, or predictive modeling within the financial services industry
• Proven track record of working with large-scale datasets and deriving actionable insights for risk management
• Strong programming proficiency in Python, R, or SQL
• Experience with credit risk modeling, fraud detection, or regulatory compliance in banking
• Ability to communicate complex data findings to non-technical stakeholders

Preferred Qualifications

• Advanced certifications in data science (e.g., AWS Certified Machine Learning or Google Professional Data Engineer)
• Experience with big data technologies like Hadoop, Spark, or cloud platforms (AWS, Azure, GCP)
• Domain knowledge in credit strategies, portfolio management, or Basel regulations
• Prior work at a major financial institution handling cross-line-of-business data integration
• Publications or contributions to open-source projects in predictive analytics

Required Skills

• Proficiency in Python and R for data analysis and modeling
• Expertise in SQL for querying large relational databases
• Machine learning frameworks like TensorFlow, Scikit-learn, or PyTorch
• Statistical modeling techniques including regression, clustering, and time-series analysis
• Big data tools such as Apache Spark, Hadoop, or Kafka
• Data visualization with Tableau, Power BI, or Matplotlib
• Knowledge of credit risk metrics (e.g., PD, LGD, EAD) and regulatory frameworks (Basel III, CCAR)
• Strong problem-solving and analytical thinking
• Excellent communication and presentation skills for stakeholder engagement
• Project management abilities to lead data science initiatives
• Attention to detail in model validation and documentation
• Adaptability to fast-paced financial environments
• Team collaboration in cross-functional settings
• Ethical data handling and privacy compliance (GDPR, CCPA)

Benefits

• Competitive base salary and performance-based annual bonuses
• Comprehensive health, dental, and vision insurance plans
• 401(k) retirement savings plan with company matching contributions
• Generous paid time off, including vacation, sick days, and parental leave
• Professional development programs, including tuition reimbursement and leadership training
• Employee stock purchase plan and financial wellness resources
• On-site fitness centers, wellness programs, and mental health support
• Flexible work arrangements, including hybrid options in Columbus, OH

JP Morgan Chase is an equal opportunity employer.