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Lead Data Scientist (Model Developer) / Deep Learning Practitioner Nottingham Trent House (95002), United Kingdom, Nottingham, Nottinghamshire About this role Our Data Science team focuses on the development of Machine Learning and Deep Learning solutions, to solve business problems and deliver actionable insights. We are a talented, collaborative and enthusiastic group, who use our expertise to derive insights from complex data, working in close collaboration with our business partners. This role will focus on leading the development of proprietary deep learning models in order to enhance our existing underwriting capabilities. By utilizing a mix of new and existing data, you will identify ways to improve predictive power and generate high impact customer insights. What you'll do Lead the development of next generation deep learning approaches to advance our current underwriting models, to ensure that our core lending capabilities remain at the forefront of the industry. Unlock the value in non traditional datasets by building sophisticated neural networks (e.g. LSTMs, RNNs, or Transformers). You will find novel ways to transform raw, multi modal inputs into powerful predictive features. Collaborate with business stakeholders to prioritise initiatives. You will bridge the gap between ambitious R&D and tangible in market results, driving ideas from initial prototypes through to production. Use a combination of business acumen, coding and statistical skills to navigate large amounts of data and extract actionable solutions. Work cross functionally on projects that support key business initiatives and drive sustainable growth. What we're looking for Strong experience developing and deploying deep learning models, particularly for sequential data (e.g. time series or language models). A proven track record leading model development, including setting the technical direction, project management, stakeholder comms, and mentoring junior members of the team. Experience producing and managing reliable and maintainable code in SQL/Python in a team setting, including code reviews and setting software engineering best practices. Hands on experience with modern Machine/Deep Learning frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers, including training deep learning models on GPUs or GPU clusters. Experience working with structured and unstructured data, such as text, logs, or time series and tokenisation techniques. A strong understanding of probability, statistics, machine learning and familiarity with large data set manipulation. A drive for continued learning through an internal and external focus, and an ability to prototype new techniques to assess value. Where and how you'll work This is a permanent position based in our Nottingham office. We have a hybrid working model, so you'll be based in our office 3 days a week on Tuesdays, Wednesdays and Thursdays, and can work from home on Monday and Friday. Many of our associates have flexible working arrangements, and we're open to talking about an arrangement that works for you. What's in it for you Bring us all this - and you'll be well rewarded with a role contributing to the roadmap of an organisation committed to transformation. We offer high performers strong and diverse career progression, investing heavily in developing great people through our Capital One University training programmes (and appropriate external providers). Immediate access to our core benefits including pension scheme, bonus, generous holiday entitlement and private medical insurance - with flexible benefits available including season ticket loans, cycle to work scheme and enhanced parental leave. Open plan workspaces and accessible facilities designed to inspire and support you. Our Nottingham head office has a fully serviced gym, subsidised restaurant, mindfulness and music rooms. Capital One is committed to diversity in the workplace. If you require a reasonable adjustment, please contact . All information will be kept confidential and will only be used for the purpose of applying a reasonable adjustment. For technical support or questions about Capital One's recruiting process, please send an email to .

Job Overview Lead Data Scientist (Model Developer) / Deep Learning Practitioner - Nottingham Trent House, Nottingham, United Kingdom. Permanent position in the Nottingham office with hybrid working model (office 3 days a week). Responsibilities Lead the development of next generation deep learning approaches to enhance underwriting models. Build sophisticated neural networks (e.g. LSTMs, RNNs, Transformers) using non traditional datasets. Transform raw, multimodal inputs into predictive features. Prioritise initiatives with business stakeholders and bridge R&D to production. Navigate large datasets, coding in SQL/Python, and deliver actionable solutions. Collaborate cross functionally on projects aligned with key business initiatives and sustainable growth. Qualifications Strong experience developing and deploying deep learning models for sequential data (time series or language). Proven track record leading model development, setting technical direction, project management, stakeholder communication, and mentoring. Experience producing maintainable code in SQL/Python and performing code reviews. Hands on with PyTorch, TensorFlow, or Hugging Face Transformers; training on GPUs or clusters. Experience with structured and unstructured data (text, logs, time series) and tokenisation techniques. Solid understanding of probability, statistics, machine learning, and large dataset manipulation. Continuous learning mindset and ability to prototype new techniques. Working arrangement Hybrid model: in office on Tuesdays, Wednesdays, and Thursdays; remote on Monday and Friday. Flexible arrangements are available. Benefits Competitive salary, pension scheme, bonus, generous holiday entitlement, private medical insurance, and flexible benefits such as seasonal ticket loans, cycle to work scheme, and enhanced parental leave. Equal Opportunity We are an equal opportunity employer committed to diversity and inclusion.