Associate - Machine Learning


What You'll Bring (required skills and experience):

  • 5+ years of professional/industry experience in machine learning, mathematical modeling, statistical modeling, optimization or data mining on real world problems involving large data sets - ideally in financial services related industry (e.g., banking and securities, asset management, insurance)
  • At least 2 years of experience with current data visualization applications/tools
  • A track record of academic excellence including a Master's or PhD degree from a reputable college/university in a quantitative field such as statistics, math, applied mathematics, financial mathematics, computational finance, finance, economics, econometrics, operations research, engineering, machine learning, computer science, system engineering, econophysics, physics, or another highly quantitative field
  • CFA, PRM, or FRM designation or candidate
  • Exceptional numerical and statistical ability, and experience with a combination of modern technologies and open source software
  • Required: R, Python and/or Tensorflow
  • Desired: Java, PHP, J#.Net environment, Perl, Mathematica, MATLAB, Hadoop, Spark, SAS, STATA, SPSS, RapidMinder, S-plus, ARC-GIS, Weka, NetLogo, MASON, RePast; experience using other programming and data manipulation languages (SQL, Hive, Pig, C/C++)

  • Solid MS Office skills - Excel (including macros and VBA) and Access (or SQL), storyboarding and PowerPoint at high proficiency preferred
  • Knowledge of any one visualization tool such as Tableau, Spotfire, PowerView, QlikView, D3.js or equivalent
  • Experience in developing advanced models such as multivariate regression, neural networks, support vector machines, Random Forest, Bayesian Analysis, decision trees, ANOVA and clustering/segmentation
  • Applied Machine Learning modeling expertise is required
  • A background in Deep Learning (CNN, LSTM), Natural Language Processing (Word2Vec), and Anomaly Detection is highly preferred

  • Modeling expertise in Quantitative Modelling, Quantitative Research, Statistics, Predictive Analytics/Predictive Modelling, Algorithmic Engineering, Non-parametric models, Time-series models, Non-linear dynamics, Complexity science, Text mining, Clustering, Monte Carlo and bootstrapping, Supervised and Unsupervised learning methods, Decision Trees and CART, Data Visualization or Data Mining is desirable

We look for individuals with demonstrated success in client-facing roles (consulting experience highly desired) who possess the following qualities that will contribute to our success and the success of our clients:

  • Problem-solving skills: well-honed analytical problem-solving ability coupled with business acumen to structure problems, deliver solutions and communicate the insights on a range of business problems faced by financial & other institutions through a deep understanding of current industry challenges and trends at the level needed to proactively identify clients' analytical needs and related business opportunities. Must also have strong quantitative and conceptual thinking skills, with attention to detail and accuracy
  • Entrepreneurial, driven spirit: strong ownership mindset with a focus on delivering high-quality end products, contributing to the growth of an organization and showing commitment to the success of their team. Energetic, self-starter who thrives in a collaborative, fast-paced environment who can work well on teams but will possess the knowledge and maturity to work with clients independently, initiate action, adapt to change, deal with ambiguity, and accept challenging assignments
  • Polished interpersonal and communication style with the ability to effectively communicate, persuade and clearly explain complex technical insights to a wide variety of audiences (specialists and generalist audiences at multiple levels within McKinsey and on the client side). Demonstrate commitment to building trusted relationships with clients
  • Learning agility and ability to quickly understand and secure data from a variety of systems and to apply learnings from one business situation to other with heightened sense of urgency and the ability to handle multiple priorities
  • Experience/exposure to financial services industry (venture capital, investment banking, commercial banking, strategy and/or financial research), in particular working with capital markets products (equities, rates, credit, FX and commodities), and experience with different categories of enterprise risk (credit counter/party, compliance, operational, liquidity and market risk)
  • In depth understanding of risk measurement frameworks, including the ability to identify and communicate risk concentrations and key drivers of risk and capital via presentations or reports
  • Working knowledge of Generally Accepted Accounting Principles (GAAP), Basel III, Dodd-Frank Act Stress Testing, and bank accounting/regulatory reporting requirements
  • Ability to travel up to 50-75%

Who You'll Work With

McKinsey has growing consulting opportunities to help clients with the practical application of machine learning in credit modelling, fraud detection, AML, etc. You will work closely with the client team to assess/diagnose business opportunities, identify and prioritize gaps in business performance and develop and implement solutions that leverage data to achieve sustainable success.

What You'll Do

How You'll Contribute as an Associate:

  • Apply deep technical knowledge of machine learning to serve as a strategic consultant to clients, work in a demanding but highly collegial and collaborative environment
  • Develop and maintain consultative relationships with key business stakeholders, proactively identifying and addressing needs
  • Demonstrate knowledge of business success drivers, industry trends, regulatory issues, and competitive marketplace
  • Collect, clean and analyze quantitative data to develop critical insights and pragmatic data solutions for our clients' issues
  • Source, scrub, and join relevant public, commercial, and proprietary data sources
  • Integrate and mine large data sets, connecting data from disparate sources to identify insights and patterns using traditional as well as predictive and prescriptive analytics
  • Analyze and visualize financial and operational data through the development of interactive descriptive analytics dashboards
  • Design and create advanced predictive models (e.g., support vector machines, neural networks, decision trees) allowing our clients to make more informed business decisions (preferably using Python and TensorFlow)
  • Produce and deliver analytic insights, findings and recommendations in succinct, compelling presentations to a team of colleagues and clients
  • Provide mentor-ship and training to junior colleagues while maintaining progress on all initiatives under minimal direct supervision

See Inside the Office of McKinsey

McKinsey helps clients facing a whole range of business questions to identify, understand, and analyze their options and make good strategic decisions. Whether it’s a tech giant contemplating a large investment, a pharma company expanding overseas, or a nonprofit looking to increase donations, the McKinsey team knows the ins and outs of the industries it serves and helps its clients tackle tough challenges and make big decisions.

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