Risk Scientist Manager

Job Overview:

The Risk Management Scientist will play a crucial role in assessing, analyzing, and mitigating risks across the organization. Involves deep expertise in data science to lead risk-related projects, offering technical insights and delivering actionable strategies to manage and mitigate risk. You will work closely with senior leadership, including the Chief of Risk, to support the organization’s overall risk strategy.

As an individual contributor with a strong data science background, you will leverage quantitative methods, predictive modeling, and advanced analytics to identify and analyze risk exposures, trends, and potential threats to the organization. The ideal candidate will be highly analytical, proficient in advanced data science techniques, and capable of interpreting complex data into clear, actionable insights.

Key Responsibilities:

• Risk Analysis & Modeling: Design, develop, and implement advanced data science models to assess, quantify, and predict risks within the organization. Provide insights on key risk areas and advise on mitigation strategies.
• Data-Driven Decision Making: Utilize big data analytics, machine learning, and statistical modeling to drive data-backed risk management strategies across departments.
• Risk Reporting: Generate clear and comprehensive reports to communicate risk analysis findings to senior leadership, especially the Chief of Risk, and help guide organizational decision-making.
• Collaboration: Partner with various internal teams (e.g., finance, operations, compliance) to gather necessary data, interpret findings, and ensure risk strategies are effectively implemented.
• Research & Development: Stay up-to-date with the latest developments in risk management, data science, and predictive analytics to continuously improve and innovate risk management processes.
• Project Leadership: Lead and manage risk-related data science projects from conceptualization to execution, ensuring successful implementation of risk mitigation strategies.
• Quality Assurance: Ensure the accuracy, integrity, and consistency of risk data and analytics models by reviewing data sources, methodologies, and results.
• Stakeholder Communication: Present complex risk findings and models to stakeholders, including executives, in a clear and accessible manner.

Qualifications:

• Education:
  • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, Risk Management, Economics, or related field.
• Experience:
  • Minimum of 8 years of experience in risk management or related roles, with a strong focus on data science and quantitative analysis.
  • At least 3 years of experience in a managerial or leadership capacity within a risk management framework.
  • Proven track record of working on complex risk projects, especially in environments requiring advanced data science techniques and methods.
• Technical Skills:
  • Advanced knowledge of data science tools and techniques, including machine learning, statistical modeling, and predictive analytics.
  • Proficiency in programming languages such as Python, R, or SQL.
  • Familiarity with risk management frameworks and tools (e.g., Monte Carlo simulations, Value at Risk models, etc.).
  • Experience with data visualization tools (e.g., Tableau, Power BI) to present complex information clearly.
• Soft Skills:
  • Strong problem-solving abilities and critical thinking skills.
  • Excellent communication skills, both written and verbal, with the ability to explain complex data science concepts to non-technical stakeholders.
  • Ability to collaborate effectively across teams and influence decision-makers.
  • High attention to detail and a strong analytical mindset.
• Desirable Skills/Certifications:
  • Certification in Risk Management (e.g., FRM, PRM) is a plus.
  • Experience with cloud-based data platforms (e.g., AWS, Azure) and big data tools.

Key Competencies:

• Analytical Thinking: Strong ability to analyze and interpret large datasets to assess risks and provide data-driven insights.
• Leadership: Capability to guide risk management strategies and influence senior leadership through data and technical expertise.
• Innovation: Continuously look for new ways to improve risk management practices using the latest advancements in data science and technology.
• Collaboration & Influence: Skilled at working cross-functionally with other teams and departments to achieve common goals.