Lead Analyst

United Kingdom

Marks & Spencer

We operate a family of businesses, selling high-quality, great-value own-brand products in the UK and internationally.

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Team

Join the M&S Property Data Science and Insight Team and help us in establishing a culture of informed and strategic decision-making that is powered by bespoke, best-in-class analytical solutions.

Our key responsibilities are:

  • Informing and directing the Leadership Team on ways to improve delivery of the Estate Rotation Plan
  • Producing actionable insight that drives continuous performance improvement
  • Instilling a culture of data-informed decision-making and a smarter-working environment
  • Collaboration with data and insight teams across M&S to share knowledge and best practice

We operate under the model of ‘analytics as a delivery service’, where we aim to craft analytical solutions that are delivered on time, as intended, and with visibility on progress throughout.

We are a small team of 4, necessitating the need to work quickly, efficiently and intelligently in delivering an effective pipeline of output to the business. The role does not include line management responsibilities, but you will provide guidance and support to your Data Analyst and Data Manager colleagues.

  

 

Department

The Location Planning arm of the Property Department supports the company in determining the size, type and location of our stores, looking both inwards at our current set up and performance, and outwards to our planned future make-up as we deliver our Estate Rotation and Modernisation plan. As a result, the analytical opportunities open to us are uniquely diverse and challenging, with a solution space that covers Data Analysis, Data Science, Business Analysis and complex data modelling.

 

Example business problems supported by the team include:

  • What is the optimal strategy for choosing a new site location, size and type?
  • Which factors influence market share, and what are the headrooms for us for any given site and collections of sites?
  • How can we better predict sales recapture when modelling store closures, and sales deflection when a new store opens?
  • How can we measure the impact of site renewals?
  • Which competitors support our sales performance by being closely proximate, and which are detrimental?
  • Which factors influence the take-up rates of SYW across the Estate, and how can we best utilise that channel to improve our omni-channel offering?
  • How should we select our stores for Renewal investment? What is the best data led way to prioritise?
  • How do we ‘democratise’ data across Property, providing better access and analytical support?

 

 

Role

Working alongside the Property Data Science and Insight Manager, the Lead Analyst will play a critical role in developing and delivering the insight strategy for the Property function. You will engage senior stakeholders to understand where creative, technical analysis can provide compelling solutions to a wide range of business problems, converting the requirements into a steady pipeline of constant throughout in analytical projects ranging from:

  • Business case creation and evaluation (pre-post comparisons, such as A/B Testing in our PIR frameworks)
  • Constructing high-quality, interpretable descriptive and predictive models
  • Internal consulting support through the creation and structuring of broad analytical frameworks
  • MI and Controls Reporting development

 

The emphasis throughout will be on delivering practical and actionable insight that highlights opportunities for improvement and provides smart solutions for getting there.  

 

As an applicant, you will need:

  • A track record of delivering business-impacting problem-solving through analysis and innovative thinking
  • A track record of establishing strong relationships built around trust, integrity and open dialogue
  • The ability to switch between looking at the specifics of the ‘what’ to the higher-level picture of the ‘so-what’?
  • To be comfortable presenting your work and supporting thinking to senior stakeholders
  • A willingness to methodically and creatively interrogate existing ways of working and devise and deliver technical (but comprehensible) solutions that unlock greater and more efficient returns
  • A willingness to invest in developing yourself and the team around you

 

                                                                                             

Skills & Experience

Essential:

  • Demonstrable analytical problem-solving mindset
  • Practical experience - 3+ years in an analytical role
  • Technical experience & proficiency
    • SQL & complex data mining
    • Complex dataset modelling
    • Conversant in a programming language (Python, R, Java)
    • Training in statistical fundamentals
    • Data Visualisation (Power BI, Tableau, Spotfire etc.); advanced Excel
  • Highly numerate (ideally a numerical degree or A-Level maths)
  • Strong communication and stakeholder management skills
  • Passion for problem-solving

 

Desirable:

  • Practical Data Science experience
  • Solid grasp of Data Visualisation design theory
  • Solid grasp of Business Analysis fundamentals
  • Familiarity with Agile and Design Thinking methodologies
  • Training in probabilistic modelling

 

Beneficial:

  • Retail industry experience
  • Consultancy experience
  • Experience in any of Estates Planning, Energy Management, Facilities Management

 

 

Key Relationships and Stakeholders

 

  • Property Estates team
  • Property Asset Management team
  • Property Finance
  • Property Programme Office
  • Maintenance and Asset Care team
  • Store Design team
  • Store Development team
  • BU Customer Insight teams
  • Digital & Data team
  • Retail and Property Leadership Team
  • Geolytix, CACI and other 3rd party data providers

 

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

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Tags: A/B testing Agile Consulting Data analysis Data Mining Excel Finance Java Power BI Python R Spotfire SQL Statistics Tableau Testing

Region: Europe
Country: United Kingdom

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