Lead Data Analyst - Economics

The Economist Intelligence Unit (EIU) is a world leader in global business intelligence. We help businesses, the financial sector and governments to understand how the world is changing and how that creates opportunities to be seized and risks to be managed.

At our heart is a 50-year forward-looking global forecast of the majority of the world’s economies. We seek to analyze the future and deliver that insight through multiple channels and insights, allowing our clients to make better business, investment and policy decisions.

We’re changing, embedding alternate data sources such as GPS and satellite data into our forecasting, products will increasingly be tailored to individual clients, driven by some of the most innovative data in the market.

The EIU is recruiting for a lead data analyst with a background in econometric modeling/statistical analysis with some managerial experience, to work within the quantitative economics – an international team of data analysts, econometricians and data scientists – that is in charge of The EIU’s key economic data products and the statistical/econometric model assets that powers the forecast and analysis published in the EIU’s core offerings. 

How you will contribute:

• Manage the EIU’s quantitative methodologies and econometric models used for forecasting and data analysis across the EIU, ensuring the accuracy, reliability and consistency of economic data and forecasts

• Perform essential maintenance and quality assurances for the production of the economic & industry data and forecasting models

• People manage junior and senior data analysts, mentor and coach them as required, carry out periodic performance reviews and provide feedback, support team management to shape their career and skills development programs
• Manage the data service desk, responding to queries directly related to the data and methodology from both internal and external clients
• Through this process, work to resolve data and modelling issues that arises outside of the publication schedule
• Support development of new models and upgradation of existing methodologies, as per business requirements
• conduct necessary review of relevant literatures
• contribute to solution design of the modeling architecture
• support and manage solution implementation, with help from the delivery manager
• Support the team achieve necessary data and model governance criterias 
• Work closely with the Data Engineering team to setup and maintain the analytics workflow and model validation workstreams and model deployment in production, and relevant quality assurance

• Support and lead diverse range of bespoke predictive analytics and quantitative research projects, end-to-end with data-collection, data cleaning, analytics and econometric modelling tasks 

• Work closely with the Director and Senior Manager to develop and implement divisional strategies and action plans

The ideal skills for this role include:

• An advanced degree in statistics, mathematics, applied econometrics, applied economics, business economics or a related quantitative field
• 4-5 years of proven experience in statistical analysis and applied econometric modelling, especially in any of the following areas
  • Time series models - ARIMA,Vector Autoregression, Co-integration, Error Correction Models, IRF
  • Mixed effect, multilevel, hierarchical models
  • Unsupervised learning techniques like PCA and Factor Analysis
  • State space models like Dynamic Factor models (for applications like Nowcasting)
  • Ridge/Lasso/Net-Elastic Regressions
  • Logistic Regressions and similar classification models
• Experience with machine learning models or estimating econometric models with ML elements, like cross validations and bootstrapping would be good to have
• Prior domain experience in macro forecasting, economics or financial research would be good to have
• 1-2 years of proven experience in people management, either in small teams or projects with multiple stakeholders, is essential
• Prior experience in applications of empirical and non-empirical methods (structural/semi-structural) for the purpose of macroeconomic forecasting will be good to have
• Proficiency in any statistical softwares (Eviews/Stata) or programming languages like R/Python/Matlab is preferable
  • Experience with R-Shiny and R-Markdown will be good to have (if proficient in R)
• Advanced Excel skills – experience with formula auditing, manipulating and cleaning large data sets in excel, knowledge of advanced functions like VLookup/HLookup, Match, Offset, Index, Indirect, Nested If-Then-Else and similar
• Proficiency with SQL, to query databases will be good to have
• Excellent communication skills