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Technical Lead, Data Scientist

Resource Solutions
Job LocationLondon
EducationNot Mentioned
SalarySalary negotiable
IndustryNot Mentioned
Functional AreaNot Mentioned
Job TypePermanent, full-time

Job Description

Technical Lead, Data Scientist

  1. Purpose
Our Client has a growing Analytics and Data Science (A&DS) team operating within a London Digital Hub whose role is to maximise the value the organisation gets from data. We are searching for a technical lead who will support and enhance the growing Hub, providing deep technical understanding of data science tools, techniques and technologies, and guide more junior members through data science projects.
  1. Dimensions
This role requires a candidate with at least five years data science experience, ideally within insurance or financial services. The candidate will have operationalised data science projects realising tangible commercial value.As well as the hard skills required (see competencies section below) it is of the utmost importance that the candidate can bring their personality to the team, mentoring junior members, review and validate data science work, and manage projects with stakeholders at all levels, showing passion and innovation in how to use data to solve key problems. Key dimensions to this role are:
  1. Machine learning and predictive analytics - Real world application of machine learning algorithms in a B2B setting, including regression, random forest, naïve Bayes, K-nearest neighbour, XGBoost and neural networks. Experience in Tensorflow is ideal.
  2. Customer analytics - The candidate will have experience with a variety of customer analytics, such as reducing churn, driving new business/upselling, improving engagement or customer experience, B2C pricing and elasticity, etc. ideally in an insurance or financial services environment.
  3. Natural language processing - Experience using NLP techniques, in particular text classification, entity tagging, network maps, sentiment analysis and semantic analysis with large volumes of text data.
  4. Data engineering and ingestion - The candidate will be comfortable preparing data in Python/R and tools such as Dataiku & Alteryx. As the A&DS team is reliant on external as well as internal data, the role will involve a familiarity with sourcing and collecting data from the web, via web scraping and web crawling.
  5. Project and stakeholder management - The candidate will have managed data science projects from scoping to delivery, ensuring other data team members (engineers, analysts and junior data scientists) are aligned and able to deliver. They will also be familiar with code repositories (TFS/Git) and principles of code management.
  1. Key Result Areas.
  2. Support the London Digital Hub in delivering operational data science applications to teams across the business, for example the underwriting workbench platform.
  3. Ensure the accuracy and predictive power of algorithmic solutions within these projects, managing the deployment and maintenance of machine learning solutions in claims, underwriting and elsewhere as required. Document and manage the audibility/transparency of these approaches to minimise compliance risks.
  4. Scope and support the pipeline of working coming into the lab, in particular working closely with the teams data engineers to ensure consistent, governed and efficient throughput of data.
  5. The Operating Environment and Context of the Job
The candidate is entering the team at a critical point in its development, with the Digital Hub gaining traction. The candidate will be comfortable working closely with data analysts and data engineers but needs to have a forward looking disposition - how can data science be used to drive change beyond traditional analytical methods and ways of thinking At the same time they need to be able to speak the language of an organisation which is increasingly receptive to data science but not always familiar with its praxis.Job Specific Competencies:Identify the key 5-6 competencies specific for the job.
  1. Machine learning and predictive analytics
  2. Customer analytics
  3. Natural language processing
  4. Data engineering/ingestion
  5. Project/stakeholder management

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