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Data Scientist (GenAI - Customer Identity)

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and 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. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person.

Our Data Environment

Our Data teams are aligned to divisions covering the following Banking Services & Products, Customer Identity & Financial Crime and Data & ML Engineering. Our Data teams are excited about delivering meaningful and impactful insights to both the business and more importantly our customers. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.

Responsibilities:

  • Build, test and deploy machine learning models which will improve and/or automate decision making.
  • You will be part of a team delivering data driven solutions and insights to improve the speed, efficiency, and quality of decision-making.
  • Work proactively with technical and non-technical teams to deliver insights to support the wider business.
  • Implement comprehensive model monitoring.
  • Develop model training and evaluation pipelines to accelerate model development / deployment adhering to software development best practices (CI/CD & MLOps)
  • Engage with Engineering teams to ensure we capture data points that are relevant and useful for insights and modelling.

  • You have at least 3-4 years of experience as a professional Data Scientist
  • Python, which makes up the majority of our Data Science stack.
  • Proven experience in data science, with a focus on machine learning model development/Large Language Model (LLM) application development.
  • Experience deploying Generative AI applications to production in GCP (VertexAI) or AWS (Bedrock).
  • Demonstrable experience monitoring the performance and output quality of generative models, including assessing/mitigating hallucinations and coherence of generated content. 

Desirables:

  • Experience fine-tuning large language models is a bonus.
  • Prior experience utilising LLMs on mobile applications is a bonus.
  • Experience with open-source large language models (e.g Llama).

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 below, following a chat with one of our Talent Team:

  • Stage 1 - 30 mins with one of the team
  • Stage 2 - Take-home challenge
  • Stage 3 - 60 mins technical interview with two team members
  • Stage 4 - 45 min final with two executives

  • 33 days holiday (including public holidays, which you can take when it works best for you)
  • An extra day’s holiday for your birthday
  • Annual leave is increased with length of service, and you can choose to buy or sell up to five extra days off
  • 16 hours paid volunteering time a year
  • Salary sacrifice, company enhanced pension scheme
  • Life insurance at 4x your salary & group income protection
  • Private Medical Insurance with VitalityHealth including mental health support and cancer care. Partner benefits include discounts with Waitrose, Mr&Mrs Smith and Peloton
  • Generous family-friendly policies
  • Incentives refer a friend scheme
  • 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

About us

You may be put off applying for a role because you don't tick every box. Forget that! While we can’t accommodate every flexible working request, we're always open to discussion. So, if you're excited about working with us, but aren’t sure if you're 100% there yet, get in touch anyway. We’re on a mission to radically reshape banking – and that starts with our brilliant team. Whatever came before, we’re proud to bring together people of all backgrounds and experiences who love working together to solve problems.

Starling Bank is an equal opportunity employer, and we’re proud of our ongoing efforts to foster diversity & inclusion in the workplace. Individuals seeking employment at Starling Bank are considered without regard to race, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, physical or mental disability, military or veteran status, or any other characteristic protected by applicable law. When you provide us with this information, you are doing so at your own consent, with full knowledge that we will process this personal data in accordance with our Privacy Notice.

By submitting your application, you agree that Starling Bank may collect your personal data for recruiting and related purposes. Our Privacy Notice explains what personal information we may process, where we may process your personal information, its purposes for processing your personal information, and the rights you can exercise over our use of your personal information.

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What You Should Know About Data Scientist (GenAI - Customer Identity), Starling Bank

Join Starling Bank as a Data Scientist (GenAI - Customer Identity) and become part of our mission to revolutionize banking! We’re not your typical bank; we’re all about using innovative technology to help our customers save, spend, and manage their money more efficiently. You’ll work alongside a passionate team of more than 3,000 individuals dedicated to pushing the boundaries of what banking can be. In this role, you will build, test, and deploy machine learning models that enhance decision-making processes across the organization. With at least 3-4 years of experience in the field, you’ll take ownership of the data-driven solutions that we offer. Collaborating with both technical and non-technical teams, you will help streamline the processes that support our customers and the wider business. Your proficiency in Python will be key, along with your proven ability to implement and monitor Generative AI applications in platforms such as GCP or AWS. Here, we don’t just crunch numbers; we make meaningful impacts that resonate with our core values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness. If you’re ready to thrive in a flat organizational structure that encourages innovation and collaboration, then we’d love to hear from you! Plus, enjoy a hybrid working model that lets you balance your work-life effectively while being part of a team that empowers and supports you. Your journey at Starling can be as impactful as the banking transformation we aim for.

Frequently Asked Questions (FAQs) for Data Scientist (GenAI - Customer Identity) Role at Starling Bank
What are the primary responsibilities of a Data Scientist (GenAI - Customer Identity) at Starling Bank?

As a Data Scientist (GenAI - Customer Identity) at Starling Bank, you will be responsible for building, testing, and deploying machine learning models aimed at improving decision-making processes. This involves working proactively with various teams to deliver meaningful insights and data-driven solutions tailored to enhancing customer experience.

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What qualifications do I need to become a Data Scientist (GenAI - Customer Identity) at Starling Bank?

To qualify for the Data Scientist (GenAI - Customer Identity) position at Starling Bank, you should have at least 3-4 years of experience in data science, a strong proficiency in Python, and a focus on machine learning models or Large Language Model (LLM) applications. Experience with deploying Generative AI applications on platforms like GCP or AWS is also essential.

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What tools and technologies should a Data Scientist (GenAI - Customer Identity) at Starling Bank be familiar with?

Data Scientists (GenAI - Customer Identity) at Starling Bank typically work with Python as their main programming language. Familiarity with machine learning frameworks and tools, as well as cloud services like GCP (VertexAI) and AWS (Bedrock), is crucial for success in this role.

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How does Starling Bank support professional growth for Data Scientists?

Starling Bank fosters a culture of continuous learning and development, encouraging Data Scientists to engage in innovative projects and collaborate across teams. Additional benefits include insights from industry experts, resources for studying relevant technologies, and the opportunity to work on impactful initiatives that enhance both personal and professional growth.

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What is the work culture like for Data Scientists at Starling Bank?

At Starling Bank, the work culture is fast-paced, innovative, and collaborative. Data Scientists thrive in an open environment where they can easily communicate with their colleagues and take ownership of their projects, ensuring they make meaningful contributions towards transforming the banking experience.

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Common Interview Questions for Data Scientist (GenAI - Customer Identity)
Can you describe a machine learning project you worked on as a Data Scientist?

When answering this question, focus on the objectives of the project, the machine learning techniques you utilized, and the results achieved. Provide specific metrics or outcomes that demonstrate the impact of your work.

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How do you approach model evaluation and performance monitoring?

Discuss the frameworks you use for evaluation, like cross-validation, A/B testing, or performance metrics specific to your models. Highlight your experience in monitoring model drift and adapting accordingly.

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What are some challenges you've faced in deploying machine learning models, and how did you overcome them?

Explain the specific challenges, such as integration with existing systems or scaling issues, and your strategies for resolving them, showcasing your problem-solving skills and adaptability.

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How do you stay updated on trends and advancements in data science and AI?

Share your methods for continuous learning, like attending conferences, participating in online courses, or following thought leaders in data science, to demonstrate your commitment to staying current.

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Can you explain the concept of overfitting in machine learning?

Clearly articulate what overfitting is, using examples. Discuss techniques for preventing overfitting, such as regularization or cross-validation, showcasing your technical understanding.

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What experience do you have with Generative AI applications?

Detail your experience with Generative AI, discussing specific projects where you've applied LLMs or generative models, and the impact they had on your workflows or the results achieved.

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How do you handle missing data in a dataset?

Discuss various strategies you use for missing data, such as imputation techniques or deletion methods, demonstrating your analytical thinking and attention to detail.

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Describe your experience with Python and its libraries for data science.

Share specific libraries you've used, like Pandas, NumPy, or TensorFlow, and how they have contributed to your projects. Highlight your proficiency level and practical applications.

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What is your experience with data visualization to communicate insights?

Talk about tools you use for visualization, such as Matplotlib or Tableau, and illustrate how effective visualizations have augmented your data storytelling and decision-making processes.

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How do you ensure effective communication with non-technical stakeholders?

Explain your strategies for making technical jargon accessible, such as using simple analogies or visuals, to facilitate clear communication and understanding among diverse teams.

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Our mission is to create a bank that guides and informs you towards better decisions. By giving you real-time insights, Starling enables you to understand your financial life in a whole new way.

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Full-time, hybrid
DATE POSTED
April 1, 2025

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