Data Science Manager

Overview

This individual will support the IB Global PS Analytics and Insights agenda helping with sDNA and leveraging that information to deliver solutions for driving actionable insights for the IB Sector and key Markets. Key responsibilities will be to understand data availability in different markets, develop solutions using advanced analytics, artificial intelligence, machine learning and deep learning projects to support perfect store agenda Optimizing and integrating different data sets to enable the undertaking of advanced analytics to identify growth opportunities.

Responsibilities

As the data scientist, this individual will be responsible for development and deployment of data science modeling frameworks using Statistical modeling (Machine Learning as appropriate) for sDNA and cDNA for France , prediction of store potential, store segmentation, assortment optimization, asset deployment, audience profiling, etc.

Data Analysis and Modeling: Analyzing complex datasets to extract valuable insights and identify trends/patterns. Develop and implement predictive and prescriptive models to support business decision-making. Ensure data quality and integrity throughout the analysis process.

• Working with huge and varied datasets to find sources of strategic advantage in go-to-market and execution, with a focus on Organised Trade, Traditional Trade and Away-From-Home
• Clean and prepare data for use in advanced analytics, artificial intelligence, and machine learning projects
• Build analysis in Python with accompanying documentation
• Designing and using algorithms and building predictive & prescriptive models needed to automate execution and the generation of insights, to enable efficiencies that allow colleagues to focus on value added tasks.
• Provide responses to ad-hoc follow-ups when double-click (additional questions) required with tables/charts for relevant data
• Developing our internal capability to derive insights from structured data, unstructured data through location intelligence, machine learning, collaborative filtering, etc. to unlock new sources of insight that can be used to drive competitive advantage.

Qualifications

• Bachelor's or advanced degree in CS/Technology, for instance a Master’s degree or PhD in data science or math (, Computer Science, Mathematics, Statistics, Data Science) or equivalent experience