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Data Scientist (Computer Vision)

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.

This role sits within the Customer Identity & Financial Crime data division. This team is responsible for the deployment of analytical solutions and machine learning models to prevent and detect financial crime and better understand our customers. This role specifically will focus on the customer identity domain, with a focus on identity verification, KYC and OCR technologies.

Responsibilities:

  • Build, test and deploy machine learning models which will improve and/or automate decision making
  • Collaborate with engineering, cyber, risk and operational teams teams to identify appropriate data points that are relevant for modelling, using this insight to inform the creation of predictive models
  • Conduct exploratory data analysis to identify trends, patterns and anomalies in customer identity data
  • Continuously monitor the performance of identity models in production and refine them to improve accuracy, scalability and efficiency

We’re 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:

  • Demonstrable industry experience Data Science/Machine Learning in Computer vision-related projects:
    • Identity verification / KYC
    • Computer vision
    • OCR
    • Anomaly detection
  • Excellent skills in Python and SQL
  • Experience with libraries such as Scikit-learn, Tensorflow, Pytorch
  • Strong data wrangling skills for merging, cleaning and sampling data
  • Strong data visualisation and communication skills are essential
  • Understanding of the software development life cycle and experience using version control tools such as git
  • Demonstrable experience deploying machine learning solutions in a production environment

Desirables:

  • Experience with AWS/GCP
  • Desire to build explainable ML models (using techniques such as SHAP)
  • Familiarity with data privacy regulations and experience in applying these to model development

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 - 45 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
  • 25 days holiday (plus take your public holiday allowance whenever 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
  • 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 will collect your personal data for recruiting and related purposes. Our Privacy Notice explains what personal information we will process, where we will 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 (Computer Vision), Starling Bank

Join Starling Bank as a Data Scientist specializing in Computer Vision and become part of the UK's first and leading digital bank! At Starling, we are passionate about leveraging technology to create a user-friendly banking experience that empowers people to save, spend, and manage their money effortlessly. In this role, you'll collaborate with cross-functional teams to build, test, and deploy innovative machine learning models focused on enhancing identity verification, KYC, and OCR technologies. Your work will be instrumental in preventing financial crime and providing meaningful insights that directly benefit our customers. With a flat structure and a vibrant, open culture, you'll have the opportunity to take ownership of your projects and drive impactful results. We are not just looking for specific qualifications—what matters most are your skills, attitude, and a passion for technology and data. You’ll be part of a talented team that values collaboration, creativity, and rigor as we tackle challenges in the financial sector. A hybrid working environment allows for flexibility while ensuring that you can connect and interact with your colleagues. If you have experience in the realms of data science, machine learning, and computer vision, we would love to hear from you. Be ready to innovate and contribute to shaping the future of banking with us at Starling!

Frequently Asked Questions (FAQs) for Data Scientist (Computer Vision) Role at Starling Bank
What are the primary responsibilities of a Data Scientist (Computer Vision) at Starling Bank?

As a Data Scientist (Computer Vision) at Starling Bank, your primary responsibilities include building, testing, and deploying machine learning models aimed at improving decision-making processes within the financial crime prevention domain. You will collaborate closely with engineering and operational teams to identify relevant data points for modeling, conduct exploratory data analysis, and continuously monitor and enhance the performance of models to ensure accuracy and efficiency.

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What qualifications are required for the Data Scientist (Computer Vision) position at Starling Bank?

While specific qualifications are not strictly required for the Data Scientist (Computer Vision) position at Starling Bank, we seek candidates with demonstrable experience in data science/machine learning, particularly in computer vision-related projects. Proficiency in Python and SQL, along with experience using libraries such as Scikit-learn, Tensorflow, and Pytorch, are essential. Strong data wrangling and visualization skills, a solid understanding of the software development lifecycle, and experience in deploying machine learning solutions are highly valued.

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How does Starling Bank support employees' growth in their Data Scientist (Computer Vision) roles?

Starling Bank is committed to employee growth and development. As a Data Scientist (Computer Vision), you will benefit from an open culture that encourages collaboration, knowledge sharing, and innovation. The bank offers opportunities for continuous learning, mentorship, and participation in initiatives such as volunteering and wellness programs. Additionally, the flat organizational structure empowers you to take ownership of your work while receiving support from your team.

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What tools and technologies will a Data Scientist (Computer Vision) at Starling Bank work with?

In the Data Scientist (Computer Vision) role at Starling Bank, you will work with various tools and technologies such as Python, SQL, and machine learning libraries like Scikit-learn, Tensorflow, and Pytorch. Familiarity with cloud platforms such as AWS or GCP, as well as experience in data privacy regulations, are advantageous. Additionally, you may engage with tools for data wrangling, visualization, and version control like Git.

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What does the interview process look like for the Data Scientist (Computer Vision) position at Starling Bank?

The interview process for the Data Scientist (Computer Vision) role at Starling Bank consists of several stages designed to facilitate a thorough understanding between you and the company. Expect an initial chat with our Talent Team, followed by a take-home challenge. Successful candidates will then have a technical interview with team members and a final interview with executives. This conversational process allows you to ask questions and learn more about our culture and operations.

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Common Interview Questions for Data Scientist (Computer Vision)
Can you explain a machine learning project you worked on that involved computer vision?

When answering this question, detail a specific project, outlining the goal, your role, the techniques used, and the outcomes achieved. Highlight how you approached the problem and discuss any challenges you faced and how you overcame them, demonstrating your problem-solving skills and innovative thinking.

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What is your approach to feature selection in machine learning models?

Describe your methodology for feature selection, including techniques you've used such as correlation analysis, recursive feature elimination, or domain knowledge. Emphasize your ability to balance model performance and interpretability while effectively using resources.

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How do you handle model deployment in a production environment?

Explain your experience with deploying machine learning models, focusing on tools and practices you’ve used. Discuss how you ensure that models are properly monitored and maintained, and your approach to making updates based on performance metrics.

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How can you visualize and communicate the results of your data analyses?

Discuss specific data visualization tools and libraries you use, such as Matplotlib or Tableau. Highlight how you tailor your visualizations to different audiences, ensuring that complex information is presented clearly for stakeholders to make informed decisions.

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What strategies do you use for ensuring data quality during your analyses?

Detail your approach to checking the integrity and accuracy of data, including validation techniques and data cleansing processes. Discuss the importance of data quality in making reliable decisions and the roles that collaboration and documentation play in your strategy.

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Can you describe a time when you had to work collaboratively with other teams?

Provide a specific example that showcases your ability to collaborate with cross-functional teams, explaining the objectives, your role, and the outcomes. Highlight how effective communication and teamwork contributed to the project's success.

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What techniques do you use for exploratory data analysis?

Discuss the methods and tools you use for exploratory data analysis, including statistical summaries, visualizations, and hypothesis testing. Explain how these techniques help identify patterns and guide your modeling decisions.

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What experience do you have with model validation and testing?

Provide details about the validation techniques you have employed, such as cross-validation, A/B testing, or using holdout datasets. Discuss how you assess a model's performance and make informed adjustments based on validation outcomes.

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How do you stay updated on the latest trends and technologies in data science?

Share the methods you use to stay informed about developments in the field, such as attending conferences, subscribing to industry publications, participating in online courses, or engaging with community forums. Demonstrating a proactive approach to continuing education is crucial.

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Can you discuss your understanding of data privacy regulations?

Explain your knowledge of data privacy regulations such as GDPR, and how you ensure that your data science work complies with these laws. Highlight your experience in applying these regulations in practical scenarios, especially in model development and deployment.

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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
December 6, 2024

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