Executive Manager - Data Science

Job description

Overview

·  In this role, you will play a key role in driving Perfect Store and white space agendas for the DX team for PepsiCo internal teams and our bottlers, esp. with focus on building models that support these initiatives . You will be a trusted advisor to DX leaders across key markets - enabling a range of internal stakeholders to leverage data as competitive advantage on how we live our vision of identifying stores with the biggest sales potential and partner with them to accelerate growth through EDGE, prioritized investment and personalisaed execution. You will be an expert in modelling to support these priorities and will demonstrate hands-on expertise in building and application of Segmentation through Shopper Led Clustering (SLC) , Mot Valuable Store Modelling and White Space store modelling through application of Data science and advanced analytics
·  The position exists to unlock competitive advantage in go-to-market and execution through cutting edge data science, advanced analytics and AI techniques, with a focus on OT, TT and AfH. The role holder will also leverage industry best practice and shared learnings within GBS to help to establish data science and machine learning to support market projects.
Responsibilities

·  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.
·  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.
·  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.
·  Acting as an “evangelist” for data science within the Advanced Analytics team, building understanding of, and enthusiasm for, what can be achieved.
·  Develop our existing tools to build greater simulation and forecasting capability, giving our business and our retail customers confidence that we can improve returns on current execution, asset deployment and activation plans., with 3 main focus areas:

Building segmentation models (SLC)

·  Building new SLC models for existing or new markets/channels or customers – k-means algorithm etc
·  Utilising data from sDNA and/or excel data through internal data lakes or customer data
·  Building any other form of new measurement asset to answer business questions
·  Validation of model with relevant stake holders
·  Reporting of outputs of data , eg. Share of category, brand, flavour, sku, etc

  Run MVS models for key customers and channels

·  Building new MVS models for existing or new markets/channels or customers
·  Utilising 3 rd party agency data (sales, execution, POI, etc) and or sDNA to help priroitise biggest current store opportunities
·  Basic visual reporting of opportunity – eg. Matrix of store level opportunities

  Run white space models for key channels

·  Utilising 3 rd party agency data and or sDNA to help prioritise new outlets we are not ranged in
·  Basic mapping of white space outlets where necessary through either power BI and/or google maps’
Qualifications

·  ‘Best in class’ data science capabilities, for instance a Master’s degree or PhD in data science or maths.
·  5-8 years of relevant advanced analytics experience in Marketing or Commercial in either Retail, or CPG industries. Other B2C domains can be considered
·  Advanced knowledge of key data science techniques:
·  Combining data from multiple sources through APIs, Semantic Web, etc.
·  Data preparation and feature engineering
·  Supervised / Unsupervised learning
·  Collaborative Filtering
·  Location Analytics & Intelligence
·  High level of proficiency in either R or Python
·  Familiarity with query languages such as SQL or Hive
·  Able to translate complex concepts into clear stories for the business, proven through at least 18 months experience in a business facing data science role
·  Experience of defining the shape of data science projects from the initial creation of hypotheses through to delivery of results into the business