Qualifications

The ideal candidate will possess a master’s degree (or equivalent) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field such as Economics. You should have professional experience in a data science, machine learning engineering, or applied data role, demonstrating hands-on expertise in developing end-to-end data solutions.

Strong programming skills in Python are essential, including familiarity with libraries such as pandas, scikit-learn, and at least one deep learning or ML framework. Experience with cloud services, particularly AWS Bedrock or similar managed AI platforms, is highly desirable. You should have a proven track record of building and deploying ML models or data pipelines with clear evaluation criteria and documented outcomes. Knowledge of version control practices (Git), software engineering workflows, and CI/CD processes is also required.

Effective communication skills are vital, enabling you to articulate technical findings clearly to both technical and non-technical stakeholders. You should be proactive in staying current with developments in traditional machine learning and generative AI, bringing innovative ideas to the team.

Responsibilities

• Design and develop proof-of-concept solutions with measurable evaluation criteria, ensuring clarity on success metrics and decision thresholds.
• Create ETL/ELT pipelines to generate new datasets for analytical and machine learning purposes, including feature engineering pipelines for model training and reporting.
• Apply supervised and unsupervised machine learning techniques to address commercially relevant problems such as usage propensity, client segmentation, churn prediction, content recommendation, and anomaly detection.
• Develop lightweight Python-based tools and interactive notebooks to enable business stakeholders to engage with model outputs and curated data extracts.
• Prototype AI-driven internal tools utilizing LLM APIs, embedding search, and retrieval-augmented generation (RAG) techniques to demonstrate immediate business value from content assets.
• Collaborate with analytics and engineering teams to define data standards, shared datasets, and metrics that support experimental and production work.
• Work with engineering teams to define requirements for deploying PoCs into production, producing clean, well-documented code and architectural notes for seamless handoff.
• Advocate for data quality and instrumentation needs with Product and Engineering teams, contributing to the development of engineering standards and best practices.

Benefits

• Vacation allowance of 25 days, increasing to 28 days after two years of service, plus UK Bank Holidays.
• Annual personal development allowance of £1000 to support continuous learning and skill enhancement.
• Comprehensive health and wellbeing benefits, including private medical insurance, healthcare cash plans, and mental health support initiatives.
• Future planning benefits such as pension contributions starting at 4%, increasing with tenure, and life insurance coverage of four times your base salary.
• Flexible working arrangements, including the option to work remotely for one month per year, along with volunteer days, personal days, and summer Fridays.
• Recognition programs that reward colleagues through points redeemable for hotels, gift cards, charitable donations, and other rewards.
• Engagement in social activities, including team gatherings, daily breakfast, snacks, and various social events.
• Commitment to ESG initiatives such as CSR programs, environmental sustainability, and diversity & inclusion efforts, including Women at Third Bridge, Pride, and Blackbridge initiatives.
• Opportunities to participate in innovation events like hackathons, fostering a culture of continuous improvement and idea sharing.

Equal Opportunity

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