You'll work either in our North American Knowledge Center in Waltham, Massachusetts, or in our hub in Silicon Valley, focusing on Risk Analytics as part of our Risk Dynamics team, part of McKinsey's global Risk Practice.
Our global Risk Practice supports clients in many different industries facing challenges of developing and implementing tailored concepts for risk recognition, measurement, and control. Risk Dynamics is a global leader in model development, model risk management and independent validation. We solve clients' business issues with advanced analytics, our proposition to properly scoped tech enablement transformations.We enhance our clients' decision-making and risk profile by helping them to efficiently manage their model landscape, mitigate their model limitations, and optimize their risk models.
Risk Dynamics is a specialized team conducting in-depth validation and model risk advisory services for banks, asset managers, insurance firms, and other leading financial institutions. These assessments require a rigorous understanding of both underlying modeling techniques and the overall business context in which such models are being used. Our model development, model validation and model risk advisory work spans multiple risk functions, markets, operating challenges, and modeling techniques.
McKinsey fosters innovation driven by analytics, design thinking, mobile and social by developing new products/services and integrating them into our client work. It is helping to shift our model toward asset-based consulting and is a foundation for, and expands our investment in, our entrepreneurial culture. Through innovative software as a service solutions, strategic acquisitions, and a vibrant ecosystem of alliances, we are redefining what it means to work with McKinsey.
You will complete targeted quantitative analyses and build components of advanced models across the spectrum of Risk management, including prospecting, underwriting, portfolio monitoring, pricing, asset evaluation and loss mitigation.
You'll work on client projects as an integral part of engagement teams and will also be expected to contribute to internal knowledge development initiatives. You'll disaggregate and structure complex, often ambiguous, business problems and evaluate different analytical approaches to develop potential solutions. You will apply advanced analytic and quantitative tools, statistical modeling techniques, and data mining procedures to derive business insights and solve complex business problems.
Further, you will deploy statistical modeling and optimization techniques most suited for the business problem (using Python, SAS, SQL, R and other relevant tools) to improve Risk management decision making (e.g., underwriting models). You will be expected to contribute to team problem solving through the findings and insights from your analysis and help facilitate data integration and management between clients and McKinsey. Ultimately, you will contribute to the development of knowledge for the analytic group at large.
- Strong experience in and a desire to learn about risk management, including operations risk, anti-money-laundering, fraud detection and credit risk
- 3+ years of hands-on work experience using analytical tools (e.g., R, Python) to solve real world problems
- Advanced quantitative degree (MS, MA) in computer science, mathematics/statistics, engineering, or physics) or commensurate work experience
- Professional experience in one or more of the following risk analytics domains: credit risk (e.g.., line optimization), operational risk (e.g., fraud, anti-money laundering, cyber), model risk management (e.g., validation of AI), or corporate risk (e.g., portfolio optimization) Experience working in/with enterprise class data environments to access, manipulate and analyze large data sets
- Solid expertise using core statistical learning algorithms including linear models, segmentation, dimension reduction, ensemble models, SVMs, and kernel methods to analyze large structured and unstructured datasets
- Experience in one of the following machine learning / AI areas: natural language processing, deep learning, anomaly detection, graph-based techniques
- Strong experience programming (beyond simple scripts) in a modern scientific language (e.g., Python, Matlab, R)
- Experience with either TensorFlow, Spark, Java, C#, C++, or C
- Knowledge of SQL and SAS would be a plus)
- Ability to execute an analytics process start to finish from problem specification through to solution in your area of expertise
- Ability to work collaboratively in a team environment and effectively with people at all levels in an organization.
- Exceptional verbal & written communication skills, especially around translating technical knowledge into forms that can be digested by leadership and non-technical project teams
- Willingness to travel up to 80%