Applied Statistical Modelling Analyst

Aspire Data Recruitment
09 May 2017
08 Jun 2017
Contract Type
Full Time

An exciting opportunity for an experienced Statistical Analyst to join an expanding Decision Science department.

The team is focused on understanding, measuring, and predicting consumer financial behaviour and is made up of business analysts, statisticians, and quantitative scientists. From asset pricing based on sophisticated consumer segmentation schemes, to the development of operational and forecasting models, they use mathematics, psychology, economics, and raw business acumen to support the needs of a pan-European business.

The Senior Statistical Analyst is an applied statistician who builds and maintains predictive statistical models using a variety of advanced analytic tools, frameworks, and approaches. In addition, this role involves answering a broad array of general business questions through data and logical inference.

The successful individual will have the opportunity to influence a wide range of business activities, including designing and implementing operational models across multiple business channels, creating novel consumer segmentation frameworks, asset valuation in the context of both core portfolio purchases and M&A activity, and contributing to our growing industrialized behavioural science program.

This is an exciting opportunity to join a team that partners broadly within the organization and influences the company’s operational, analytic, and financial strategies. Additionally, this role is part of a thriving, pan-European modelling community devoted to using the best elements of applied and theoretical work from statistics, psychology, management science, and behavioural economics to understand the fundamental drivers of consumer financial behaviour.

Experience Required

  • Extensive statistical or analytic experience with a financial services, high-technology, pharmaceutical, or biotechnology firm
  • Expertise with formal statistical methodologies, including strong knowledge of two or more of the following: logistic regression, generalized linear models, categorical data analyses, ANOVA and regression models
  • Proficiency with base SAS and SAS/STAT, or a related technology (e.g., R, Matlab, or IDL)
    Familiarity with longitudinal and outcome-based modelling, the logic of credit scoring, and NPV and IRR analyses
  • Previous coaching and mentoring experience with junior analysts, especially around statistical methodology, hypothesis testing, and experimental design
  • A degree in business or a quantitative science (e.g., economics, math, finance) with coursework in statistics would be preferred