- Advanced quantitative degree (Master's or PhD in computer science, mathematics/statistics, engineering, or physics) with a demonstrated track record of publications
- 3-5 years of hands-on work experience in statistical or heavy numerical analyses
- Exceptional communication skills, especially around translating technical knowledge into forms that can be digested by leadership and non-technical project teams
- Broad experience applying analytical techniques in innovative ways to solve a variety of problems across industries or functional areas
- Distinctive expertise in one of the following machine learning/AI areas : natural language processing (NLP), deep learning, anomaly detection, graph-based techniques, neural networks or model validation
- Strong experience in one of more of the following risk analytics domains : corporate risk modeling, privacy/regulatory analytics (e.g., GDPR), cyber risk, and/or retail banking (e.g., consumer credit, fraud management, anti-money laundering, and operations risk)
- 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
- Very strong experience programming (beyond simple scripts) in a modern scientific language (e.g., Python, Matlab, R) and experience with TensorFlow, Spark, Java, C#, C++, or C. Knowledge of SQL and SAS would be a plus
- Strong experience in, or a desire to learn about risk management, including operations risk, anti-money laundering, fraud detection and credit risk
- Demonstrated business intuition and clear expertise in analyses
- Ability to describe and execute and analytics process start to finish from model to solution in their area of expertise, including when and why they favor specific approaches
- Willingness to travel up to 80%.
Who You'll Work With
You'll work either in our North American Knowledge Center in Waltham, MA, or NYC focusing on Risk Analytics. Our global Risk Practice supports clients in many different industries facing challenges of developing and implementing tailored concepts for risk recognition, measurement and control.
What You'll Do
You will work directly with clients to conduct a hands-on rigorous quantitative analysis, including getting the data, cleaning it and exploring it for accuracy.
Once data is transformed, you will deploy statistical modeling and optimization techniques most suited for the business problem (using Python, SAS, SQL, R and other relevant tools). You will advise client teams on analytic options to address their specific needs, including discussing potential approaches to problems, associate costs, trade-offs and recommendations. You will interpret outputs of statistical models and results with the team to translate input from quantitative analyses into specific and actionable business recommendations. This includes providing detailed documentation of modeling techniques, methodologies and process steps.
You will balance independent modeling and analytical work with oversight of firm teammates, including fellows and analysts (50/50 balance). You will also advance McKinsey's overall knowledge base by providing analytical rigor and problem solving to our proprietary knowledge investments, specifically in analytics or the domain area of your expertise.