Senior Data Scientist

Senior Data Scientist

We are looking for a Senior Data Scientist to join the ML/AI team. Our ML/AI team within the Data Organisation is a dynamic group of data scientists and machine learning experts passionate about using data to drive innovation. As a Senior Data Scientist on this team, you'll be at the forefront of developing and deploying machine learning and GenAI algorithms models. You'll collaborate with colleagues across the organisation to identify opportunities for automation, improve decision-making, and optimise our products and processes. This is a challenging and rewarding role where you can make a significant contribution to our mission while continuously learning and expanding your skillset in a supportive and collaborative environment.

Please note, the minimum expectation for office attendance is two days per week in our central London office.

Who are we?

We're Funding Circle. We back small businesses to succeed. At Funding Circle, we believe the world needs small businesses. That's why we've made it our mission to help them get the finance they need to grow.

With more than a decade of expertise under our belt, we've built a game-changer of a platform with cutting-edge data and technology that's reshaping the landscape of SME lending. Say goodbye to lengthy applications and hello to lightning-fast decisions! In just minutes, SMEs across the UK can get a decision, giving them access to competitive funding in a flash.

We know that good business is about good people. So we pride ourselves on providing meaningful, human support as well as fast, hassle-free processes to deliver an unbeatable customer experience.

The role

1. Develop and implement machine learning models using traditional ML and GenAI: Design, develop, and deploy robust machine learning models and algorithms to solve complex business problems, with a focus on enhancing various aspects of Funding Circle's operations and decision-making processes. Make use of Generative AI models and services when necessary.
2. Analyse data to identify opportunities to improve Funding Circle's products and processes: We collect large quantities of data as part of running our business, work with analysts and product managers to analyse that data and identify opportunities to enhance decision making and increase automation.
3. Communicate results and engage with stakeholders: Effectively communicate complex technical concepts and findings to both technical and non-technical stakeholders. Present insights and recommendations in a clear and concise manner to drive informed decision-making.
4. Mentorship and knowledge sharing: Actively participate in knowledge sharing within the Machine Learning and AI team and the wider data team, providing mentorship to junior team members and contributing to a collaborative and learning-oriented environment.
5. Continuous learning: Keep up-to-date with advancements in machine learning and artificial intelligence. Apply cutting-edge techniques and technologies to address business challenges and maintain a competitive edge in the financial technology sector.

What we're looking for

1. Data curiosity and problem solving skills: The ability and willingness to explore, understand and explain complex datasets and identify opportunities for automation and process improvements. Strong analytical and problem-solving skills to address real-world business challenges. Practical and outcome driven mindset.
2. Proven machine learning expertise: Demonstrated experience in developing and deploying machine learning models, with a strong understanding of various algorithms, including supervised and unsupervised learning methods. Additional knowledge of GenAI and LLMs is an advantage.
3. Software development skills: Strong programming experience, ideally in Python. Ability and willingness to work alongside machine learning engineers on the production implementation of algorithms and machine learning models. Experience working on production applications with software developers is an advantage. The role will require working in close contact with production systems, and quality software engineering practices are essential.
4. Data manipulation, analysis and feature processing: Proficient in data manipulation and analysis using tools like Pandas, Polars, NumPy, and SQL. Ability to work with large-scale datasets and extract meaningful insights. Experience in feature engineering and data preprocessing to optimise input data for machine learning models. Ability to handle data quality issues and outliers effectively. Solid understanding of statistical concepts and techniques for modelling.
5. Collaborative team player: Strong interpersonal and communication skills and the ability to work collaboratively in cross-functional teams, both non-technical (domain experts) and highly technical (software, data and platform engineers). Practical mindset with a focus on team outcomes.
6. Continuous learning and adaptability: Commitment to staying updated on the latest developments in data science and machine learning. Ability to work in a fast paced, high innovation environment.

Ready to make a difference?

We'd love to hear from you.