Data Scientist

6 days ago

Cardiff, Cardiff, United Kingdom Starling Bank Full time
About Starling Bank

Starling Bank is a leading digital bank that aims to revolutionize the banking industry with its fast technology, fair service, and honest values. Our mission is to provide customers with a new way to spend, save, and manage their money while taking better care of the planet.

We are a fully licensed UK bank that operates a flat structure, empowering our employees to make decisions and drive innovation. Our technologists enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech.

Job Summary

We are seeking a Data Scientist with experience working in Marketing to help us solve complex problems using Machine Learning. In this role, you will use your skills and passion for data to understand our customers and drive growth through effective marketing strategies.

Key Responsibilities
  • Develop and refine models to predict customer lifetime value (LTV) to optimize marketing investments and resource allocation.
  • Analyze customer behavior and product usage data to identify target audiences and tailor marketing efforts for maximum impact.
  • Employ causal models to evaluate the effectiveness of CRM and invite campaigns and use causal inference to personalize campaigns for individual users.
  • Contribute to building and interpreting Marketing Mix Models (MMM) to understand the best growth activities.
  • Collaborate with Data Analysts to ensure they effectively utilize the models you build.
Requirements

We are open-minded when it comes to hiring and we care more about aptitude and attitude than specific experience or qualifications. We think the ideal candidate will encompass most of the following:

  • Solid understanding of customer lifetime value (LTV) modeling and marketing mix modeling.
  • Deep understanding of statistics, especially Bayesian reasoning, and the ability to assess the accuracy of your results.
  • Experience with causal inference concepts and machine learning models for causal inference.
  • Comfortable with a variety of modeling techniques, including gradient boosting, neural networks, linear regression, and blending these approaches.
  • Expert in Python with the ability to make well-reasoned design decisions in your code.
  • Comfortable working with external data sources through APIs.
  • Ability to see the bigger picture of business processes and clearly define how models can be integrated and add value.
  • Excellent communication and visualization skills to effectively present your findings to different audiences.
Interview Process

Interviewing is a two-way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general, you can expect the following:

  • Stage 1 - 45 minutes with one of the team.
  • Stage 2 - Take-home test.
  • Stage 3 - 60 minutes technical interview with two team members.
  • Stage 4 - 45 minutes final with an executive and a member of the people team.
Benefits

We offer a range of benefits to support your well-being and career development, including:

  • 33 days holiday (including flexible bank holidays).
  • An extra day's holiday for your birthday.
  • 16 hours paid volunteering time a year.
  • Part-time and/or flexible hours available for most roles.
  • Salary sacrifice, company-enhanced pension scheme.
  • Life insurance at 4x your salary.
  • Hybrid/remote working.
  • Private Medical Insurance with VitalityHealth including mental health support and cancer care.
  • Partner benefits including discounts with Waitrose, Mr&Mrs Smith, and Peloton.
  • Generous family-friendly policies.
  • Varied social groups set up and run by our employees.
  • Perkbox membership giving access to retail discounts, a wellness platform for physical and mental health, and weekly free and boosted perks.
  • Access to initiatives like Cycle to Work, Salary Sacrificed Gym partnerships, and Electric Vehicle (EV) leasing.

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