Analytics Specialist - Risk Advanced Analytics
- Advanced quantitative degree (Masters or Ph.D. in computer science, mathematics/statistics, engineering, or physics) with a demonstrated track record of publications
- 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, deep learning, anomaly detection, or graph-based techniques
- Strong experience in one of the following retail banking domains: consumer credit, fraud management, anti-money laundering, or 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 some experience with 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 from start to finish the analytic processes in their area of expertise, including when and why they favor specific approaches
- Willingness to travel
Who You'll Work WithYou'll work either in our North American Knowledge Center in Waltham, Massachusetts, or in our newly created hub in Silicon Valley, 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, associated 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.
Meet Some of McKinsey's Employees
Danielle is one of the leaders of McKinsey’s business with retail and consumer clients. She oversees client projects and helps her teams and her clients utilize McKinsey’s resources.
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