Data Science Manager

Data Science Manager Location: London, UK (On Site) Employment Type: Full-time Company: asBuilt IQ Reporting to: Head of Data Science About asBuilt IQ asBuilt IQ is a technology company transforming how infrastructure and construction  organisations plan, deliver and manage complex operations. Our product platform brings together AI, data science, geospatial intelligence, computer  vision, optimisation and digital engineering to help customers make better operational  decisions. Our solutions address real-world infrastructure challenges including intelligent  scheduling, short-term planning, asset intelligence, computer vision, predictive analytics,  visualisation and operational performance. We operate in a fast-moving technology environment, working closely with major  infrastructure organisations to take AI and Data Science solutions from concept and  experimentation through to production deployment and measurable operational value. The Role We are looking for Data Science Manager to lead the day-to-day operation and delivery of  our Data Science function. This is a hands-on player-coach role. You will manage and  develop the Data Science team while also personally contributing to the design, development and delivery of Data Science/AI solutions. You should be comfortable writing  Python and developing models, reviewing another Data Scientist's approach, managing  delivery priorities, and explaining results to a customer. You will remain actively involved in  technical delivery, particularly on complex, high-value or early-stage problems where  hands-on Data Science expertise is required. As part of a growing technology company, you will need to be comfortable working with  ambiguity, moving quickly from problem definition to prototype, and taking ownership of  delivering solutions into production. Key Responsibilities Hands-on Data Science, AI & Technical Delivery You will remain an active practitioner and personally contribute to Data Science delivery. Responsibilities include:

• Conduct exploratory data analysis and identify patterns, relationships and  opportunities within complex operational datasets.
• Build, test and improve machine-learning, statistical and optimisation models.
• Write high-quality Python and develop analytical and modelling pipelines.
• Prototype new Data Science and AI capabilities.
• Develop optimisation approaches for scheduling, routing, resource allocation and  operational decision-making.
• Contribute directly to computer vision, predictive analytics and AI initiatives.
• Evaluate model performance and identify opportunities for improvement.
• Diagnose and resolve model, data and production-performance issues.
• Support the productionisation and deployment of Data Science solutions alongside  Engineering.
• Analyse production and customer data to understand whether solutions are  delivering the intended operational outcomes.
• Produce analyses, visualisations and evidence that communicate model  performance and business value.
• Step directly into critical project delivery when additional technical capability is  required. Data Science Leadership & Team Management
• Lead the day-to-day operation of the Data Science team.
• Manage priorities, workload, resources and delivery commitments.
• Provide technical direction and mentoring to Data Scientists.
• Review modelling approaches, code, experiments and analytical outputs.
• Establish clear technical ownership across projects and products.
• Ensure appropriate standards for experimentation, validation, documentation and  productionisation.
• Support recruitment, onboarding and professional development of Data Science  talent.
• Identify technical blockers early and actively help the team resolve them.
• Create an environment where Data Scientists can experiment while remaining  accountable for delivering production outcomes. Product Development
• Work closely with Product and Engineering to turn operational problems into  scalable Data Science capabilities.
• Contribute to the AI and Data Science roadmap across the VAULT platform.
• Translate product requirements into technical Data Science requirements.
• Rapidly prototype new capabilities to validate feasibility and customer value.
• Help transition successful experiments and proof-of-concepts into robust  production solutions.
• Identify opportunities to create reusable models, algorithms and Data Science/AI services across multiple customers.
• Balance customer-specific requirements against the need to build scalable product  capabilities.
• Evaluate emerging AI technologies and determine where they can create  meaningful product value. Customer & Project Delivery
• Act as a senior Data Science representative in customer workshops and delivery  discussions.
• Work directly with customers to understand operational processes, constraints,  datasets and desired outcomes.
• Translate complex operational problems into structured Data Science problems.
• Perform analysis required to answer customer questions and validate proposed  solutions.
• Present analytical findings, model behaviour and recommendations to technical  and non-technical audiences.
• Support pilots, proof-of-concepts and production implementations.
• Investigate issues identified during customer testing and production use.
• Proactively identify delivery risks, data-quality issues and technical dependencies.
• Ensure Data Science work is connected to measurable operational and commercial  outcomes. What We're Looking For
• Approximately 6+ years of professional experience in Data Science, Machine  Learning, AI, Operations Research or a related discipline.
• Strong hands-on Data Science/AI capability 
• Advanced Python skills and experience working directly with complex datasets.
• Strong experience developing machine-learning, statistical or optimisation models.
• Experience taking models from experimentation through to production use.
• Previous experience leading Data Scientists, ML Engineers or technical project  teams.
• Ability to review and challenge technical approaches developed by other Data  Scientists.
• Strong understanding of model validation, experimentation and performance  evaluation.
• Ability to structure ambiguous business and operational problems into analytical  solutions.
• Strong stakeholder management and communication skills.
• Experience working directly with customers, product teams or operational  stakeholders.
• Ability to switch effectively between hands-on technical work, team leadership and  customer delivery.
• Comfortable operating with significant autonomy and accountability. Desirable Experience Experience in one or more of the following would be particularly valuable:
• Scheduling, routing or resource optimisation
• Computer vision
• Geospatial analytics
• Forecasting and predictive modelling
• Generative AI, LLMs, RAG or agentic AI
• Azure or other cloud platforms
• MLOps and production model monitoring
• Infrastructure, highways, construction, transport, utilities or asset management.
• Digital twins or spatial/engineering technology
• B2B SaaS or enterprise technology environments
• Start Up environment Direct infrastructure experience is advantageous but not essential. We are particularly  interested in candidates who have successfully applied Data Science to complex operational problems.