Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Head Advanced Quantitative Sciences Tech Delivery

FCRS = GB016
London
1 year ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data (Senior Data Scientist)

Hands-on Head of Data Science - FinTech Growth

Senior Data Engineer/ Scientist

Machine Learning Engineer/Researcher - 2025 Programme

Machine Learning Engineer/Researcher - 2026 Graduate Programme

Summary The Head Advanced Quantitative Sciences (AQS) Tech Delivery is responsible for leading the strategy and delivery of major tech projects and products for our quantitative science communities. Implementing fit-for-purpose processes and state-of-the-art Statistical Computing Environment (SCE) platform and tooling supporting AQS strategy, compliant with global regulatory requirements, this role has a critical impact on development programs and trial teams, enabling over 2000 users. This role promotes positive values and behaviors, driving a mindset shift towards embracing change, fostering enterprise collaboration, continuous improvement, solutioning, and integrating cutting-edge technologies into the enterprise. This leader maintains high focus on business needs, delivering technology solutions that are intuitive through cross-functional collaboration with other departments. Job Description Major accountabilities: Accountable for end-to-end tech transformation roadmap in collaboration with IT, defining AQS implementation strategy, detailed plan and related deliverables further enabling this complex, fast changing organization whilst increasing productivity and securing high quality and compliance. Ensure delivery excellence of projects across the focus areas and supporting workstreams, including coordination of efforts, resources assignment and interdependencies . Ensure solid construct and updates of the business case (value levers and other indicators as appropriate). Establish and monitor KPIs related to transformation progress and risk management. Provide direct input and expertise in the areas of process design, risk management, governance, organizational design and compliance. Ensure high collaboration and foresighted coordination of progress and implications beyond AQS with other teams involved internally across development . Ensure high level of connectivity, transparency and collaboration with other Development and corporate initiatives and departments. Contribute to strategic long-term decision-making by AQS Leadership, driving technology investments in Development in collaboration with Senior leadership. Invested in continual learning and staying updated on emerging trends and technologies within the industry. Develop and shape a world-class computing and engineering organization, managing talent, promoting functional excellence, and recruiting and retaining high talent. Coordinate internal communication and stakeholder management on transformation progress. Emphasize scalability of solutions, ensuring that systems can grow alongside the organization’s needs. Accountable for the talent and career development of direct reports and teams, including performance management, and contribute to the development of AQS staff through onboarding, training, and mentoring. Requirements: University degree in computer science, engineering or relevant field. Preferred Masters, PhD or relevant equivalent experience. Minimum 15 years of relevant experience in project management, process re-engineering and organizational transformation delivery with a strong understanding of drug development preferred. Thorough knowledge of GxP, IT systems, QA and regulatory/clinical development process. Exposure to cloud computing platforms (e.g., AWS, Azure), data science and analytics tools (e.g., Python, R, SAS), iterative development methodologies (eg: Agile) and artificial intelligence and machine learning applications in clinical or research settings. Enterprise-level leadership perspective. Able to make the case for change, engage and influence multiple x-divisional stakeholders. Experienced team leader for global diverse teams. Track record of attracting, developing, and retaining talent and building high performance teams. Experience executing strategic planning and risk management. Skills Desired Automation, Biostatistics, Computer Programming, Cross-Functional Team Leadership, Data Analytics, Drug Development, Global Project Management, Influencing Skills, Leadership, Metadata Management, Statistical Analysis, Strategy Execution

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.