Machine Learning Quant

  • Negotiable
  • London, England, United Kingdom London England GB
  • Permanent, Full time
  • Standard Chartered Bank
  • 19 Apr 18 2018-04-19

The successful applicant will be: Machine Learning Quant to support the development of predictive models for Stress Testing and Corporate Plan

Machine Learning Quant to support the development of predictive models for Stress Testing and Corporate Plan

SCOPE OF THE ROLE: SCB's Quantitative Modelling and Analytics team is responsible for the research, development and implementation of quantitative models (particularly PPNR models) and visual analytic tools. The scope of the work covers areas such as Retail, Transaction Banking, Corporate and Investments Banking (CIB) products as well as areas such as commodities, interest rates and FX. The outputs are used for: Stress Tests Capital and Liquidity Management (CLM) Financial Planning & Analytics (FP&A)The role holder will be responsible for development, validation and documentation of quantitative models for Stress Testing and Corporate Plan in a way that meets regulatory guidance and internal requirements, managing the development through to final approval.

KEY ROLES & RESPONSIBILITIES Machine Learning Models (60%): Experience in developing scripts to programmatically source, clean, aggregate/disaggregate, transform raw data for quantitative modellingDevelop new regulatory and business-related models using Generalized Additive Models framework and other machine learning techniques. Provide robust model selection, testing and validation methodologies. Document new models to the Group standards and best industry practice, prepare submission for independent model validation Visual Analytics (20%): Develop interactive visualisations to empower business users to explore and understand model outputs (e.g., R Shiny). Develop visual exploratory analysis processes in ways that work in harmony with socialisation of quantitative models and development of 'data-driven storytelling'.Communicate important trends and patterns. Stakeholder Management (20%): Ability to work well within a team of machine learning and quantitative modellers, analyst and business experts, with a desire to take initiatives in the specification and development of solutions. Ability to meet deadlines and work under tight time scales. Be flexible and able to switch from visual analytics to quantitative modelling following stakeholders' feedback. Good verbal and written communication skills, including presentation skills. Interpersonal skills appropriate for communication within Standard Chartered Bank, external stakeholders and with the wider financial and banking community.

* Post graduate degree (preferably PhD) in Statistics, Data Science and/or related discipline with a strong programming experience (preferably in R).

* Good knowledge of machine learning and computational statistics, e.g. regression analysis, time-series analysis, smoothing techniques, random forests, etc.

* Track record of producing high quality development code and documentation including results of research and presentations for technical and non-technical audiences

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