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Lead Data Scientist (Tech sector)

Lead Data Scientist (Tech sector)

  • Location

    London, England

  • Sector:

    Part-qualified & Transactional Finance

  • Job type:

    Permanent

  • Salary:

    £55000.00 - £60000.00 per annum + bonus and excellent benefits

  • Contact:

    David Mendonca

  • Contact email:

    Davidm@cedarrecruitment.com

  • Job ref:

    JO0000006767_1554920181

  • Published:

    12 days ago

  • Expiry date:

    2019-05-10

  • Startdate:

    ASAP

  • Consultant:

    David Mendonca

A well known brand in the ecommerce Tech sector is looking for a bright and ambitious Data Scientist to accelerate their career in a fast paced, innovative business. The company's vision is to become the global marketing platform of choice connecting consumers and businesses.

This is a young and dynamic, friendly 'Tech Startup' environment.

Operating multiple brands across 22 countries and HQ in London, they also have offices across Europe. Their customers include top brands such as Amazon, Asos and Sainsburys-Argos.

Role overview:

This brilliant business is looking for an ambitious individual to join the commercial finance function as a Lead Data Scientist. This is a newly created role, supporting all local and global commercial activities across the Group.

You will be responsible for collaborating with business subject matter experts to discover the information hidden in various sources of content and data, helping us to enhance profitability. Your primary focus will be in applying complex data mining techniques and statistical analysis, to develop high quality predictive models, and make recommendations to internal stakeholders on alternative courses of action.

You'll step in to provide an authoritative voice when there are analytics questions that are particularly contentious or complex.

Key responsibilities

  • Work with stakeholders throughout the organisation, to identify opportunities of leveraging our data assets, to drive incremental gross margin.
  • Contribute to the pricing strategy of the company, driving the optimal balance between revenue, margin and quality across the product portfolio, countries and merchants.
  • Establish a "trading platform" which enables us to maximise/optimise gross margin from each of our customers, recognising changing behaviours in consumer search patterns, and associated impacts on quality conversion.
  • Monitor and drive optimal trading performance across multiple countries in partnership with the commercial teams.
  • Connect various data points, which allows us to highlight merchants that are likely to cancel or who present upsell opportunities- Thus allowing for our Account Management Teams to have more targeted focus.
  • Create self-serve automated platforms, to make models and insights available throughout the group
  • Financial modelling / forecasting of GM trends, identifying risk and opportunities.
  • Monitor and analyse business performance. Link P&L outcomes to commercial KPIs such as
    • Merchant pricing, acquisition, and churn
    • Publisher economics and traffic volumes
    • Website traffic and conversion
  • Develop new MI and KPI reporting to support strategic and tactical decision making.
  • Collaborate with the Data Science team in Europe

Experience requirements:

  • Data-driven decision-making experience essential, working with large datasets, developing KPIs for a data intensive business.
  • Business partnering with commercial stakeholders & presentational experience to a senior management audience.
  • Experience in an ecommerce/digital business is a clear advantage.
  • Experience in businesses involving either complex technology or sales a plus.

Skills we are looking for to be successful in this role:

  • Degree educated, in a quantitative field such as Computer Science, Economics, Statistics or Mathematics
  • Experience of working with SQL
  • A strong Statistical mindset, BUT with the ability to articulate complex analysis into "everyday language" back to a business audience
  • Knowledge of advances statistical techniques and concepts, with experience of implementation
  • Expertise of using SAS, Python, or Spark to build predicted models using regression and machine learning techniques
  • Highly analytical. Strong commercial analysis skills. Ability to apply logical thinking to gathering and analysing information, designing and testing solutions to problems, and formulating plans and options.
  • High quality Excel and presentation skills.
  • Commercial judgement. Experience of proactively challenging and influencing the business based on insight, knowledge and fact.
  • Proactive, can-do, resilient and adaptable.