Translate priority business and engineering questions into statistically rigorous ML projects to focus team effort on the highest value opportunities and drive data-driven decisions across design, manufacturing and enterprise operations.− Design and govern controlled experiments (A/B tests, DoE) and causal analyses to quantify model impact, guiding resource allocation and ensuring measurable ROI on analytics initiatives.− Develop, validate and maintain predictive and prescriptive models on scalable data platforms to deliver reliable, production-ready insights that reduce cost, cycle time and quality-related risk.− Curate and enrich governed data and feature stores in collaboration with Data & Knowledge Engineering to maximise model accuracy, reusability and compliance with data-quality standards.− Industrialise validated models with MLOps teams, embedding monitoring for drift, bias and performance to guarantee low-latency, high-availability services that meet safety and regulatory SLAs.− Communicate analytical findings through narrative storytelling, dashboards and decision briefs to equip technical and non-technical stakeholders with clear, actionable insight and secure buy-in for change.− Champion ethical-AI and privacy-by-design principles across the model lifecycle to protect sensitive data, mitigate bias and satisfy automotive safety and data-protection regulations.Translate priority business and engineering questions into statistically rigorous ML projects to focus team effort on the highest?value opportunities and drive data-driven decisions across design, manufacturing and enterprise operations.− Design and govern controlled experiments (A/B tests, DoE) and causal analyses to quantify model impact, guiding resource allocation and ensuring measurable ROI on analytics initiatives.− Develop, validate and maintain predictive and prescriptive models on scalable data platforms to deliver reliable, production-ready insights that reduce cost, cycle time and quality-related risk.− Curate and enrich governed data and feature stores in collaboration with Data & Knowledge Engineering to maximise model accuracy, reusability and compliance with data-quality standards.− Industrialise validated models with MLOps teams, embedding monitoring for drift, bias and performance to guarantee low-latency, high-availability services that meet safety and regulatory SLAs.− Communicate analytical findings through narrative storytelling, dashboards and decision briefs to equip technical and non-technical stakeholders with clear, actionable insight and secure buy-in for change.− Champion ethical-AI and privacy-by-design principles across the model lifecycle to protect sensitive data, mitigate bias and satisfy automotive safety and data-protection regulations
What You'll Bring
Business Knowledge− Solid understanding of how advanced analytics and machine learning create value across automotive design, manufacturing, supply-chain and finance workflows.Essential Functional / Technical Skills− Advanced qualification in a quantitative discipline such as Statistics, Computer Science or Applied Mathematics (or equivalent expertise).− Proven track record of delivering production-grade ML and analytics solutions in complex, data-rich domains (e.g., manufacturing, supplychain, finance).− Deep proficiency in Python data-science tooling (Pandas / Polars, scikit-learn, statsmodels) and SQL, together with experience of distributed-compute ecosystems (Spark, Flink), version control (Git), CI/CD and a test-driven production software discipline. − Expert knowledge of exploratory data analysis, statistical modelling, causal inference, experimental design and core machine-learning theory.− Ability to build, deploy and monitor data pipelines and end-to-end ML workflows using MLflow, Kubeflow or similar orchestration platforms.− Competence with cloud-native data stacks (AWS and AZURE) and containerised environments (Docker, Kubernetes) for scalable, low?latency delivery.− Experience in developing Generative AI models, especially multimodal including in 3D domain.− Working knowledge of BI platforms and visualisation libraries (Streamlit, Gradio, Power BI, Plotly).Personal Attributes / Competencies− Analytical storyteller who translates complex findings into clear, actionable insight for stakeholders at every level.− Passionate advocate for data ethics, privacy and responsible AI within automotive innovation.Desirable Expertise− Familiarity with Bayesian methods, reinforcement learning or graph analyticsExperience working with automotive telemetry, CAN-bus data, PLM/ERP datasets or simulation outputs.− Demonstrated engagement with the data-science community through open-source contributions, conference papers or technical talks
What We'll Do for You
We offer a wide – ranging benefits package, which includes:Structured career development framework25 days’ holiday, plus bank holiday. Annual buy & sell up to five daysEnhanced company pension schemeDiscretionary annual bonus awardPrivate medical insurance and health cash planLife assurance benefitAbility to apply for a sabbatical of up to one year after only two years’ serviceBenefits you can adapt to your lifestyle, such as discounted shoppingGenerous parental leave policiesA range of wellbeing initiatives, such as employee assistance programme and free financial & mortgage advice
Who Are We?
No restraints. No limitations. We don’t simply push boundaries. We completely rethink them. McLaren Automotive exists to create breath-taking performance road cars.It takes a community to do what we do. A diverse group of people with many areas of expertise, united by their passion to deliver visionary products and set new benchmarks. McLaren Automotive commits to equal opportunity for all. Diversity, Equality and Inclusion is at the heart of our impact, it drives our innovation and enables us to truly create something special. Join us on our journey.
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