This role focuses on using advanced analytics, machine learning, and AI to improve decision-making, reduce cost / value leakage, and support stronger delivery performance within the supply chain operations team.
Compensation
The annual pay scale for this position is between $87,870.00 - $1,63,120.00.
Responsibilities
Develop AI agents, prompt-engineered workflows and automated solutions tailored to business-specific use cases.
Focusing on business-led use cases that solve slow, repetitive, fragmented, or decision-heavy processes, with a clear link to time, quality, risk, insight, or customer benefit.
Working with teams across the value stream to identify opportunities, surface dependencies, and help shape practical pilots, standards, and scalable approaches.
Supporting responsible AI adoption by considering governance, data quality, privacy, consistency, and alignment with wider business and digital standards.
Applying statistical and machine learning techniques to operational, business and financial data to identify trends, risks, and opportunities across the project lifecycle.
Lead technical vision and architecture for AI/ML solutions across AstraZeneca’s commercial landscape, guiding advanced AI from traditional ML to generative and agentic AI, while influencing multi-functional teams and ensuring production readiness.
Responsibilities
Define and drive technical vision, roadmap and architecture for AI capabilities
Provide hands‑on technical leadership to project teams
Lead design of multi‑agent systems and LLM applications
Establish evaluation standards and quality gates
Drive model optimization and cost/latency improvements
Requirements
Bachelor's degree in Computer Science or related quantitative field
Master's or PhD in machine learning, computer science or related field
The Pricing Analyst will turn pricing, customer, product, transaction, and market data into clear insight. The role is suited to someone with strong quantitative skills who enjoys statistical analysis, modelling, data validation, automation, and communicating findings to commercial stakeholders.
Responsibilities
Analyze pricing, customer, product, and market data to identify trends, opportunities, risks, and performance drivers.
Build and improve pricing models, forecasts, segmentation analysis, sensitivity analysis, and scenarios to support commercial decisions.
Develop repeatable reporting, data validation, and analytical workflows using Excel, Power Query, Python, SQL, and BI tools.
Support monthly and quarterly price forecasting, variance analysis, and commercial performance reviews.
Advanced degree in Mathematics, Statistics, Data Science, Engineering, Economics, Operations Research, or a related quantitative field; or a bachelor’s degree with approximately 2+ years of relevant analytics, pricing, commercial operations, finance analytics, or data science experience.
Lead a team of data professionals within the Group Responsible and Strategic Procurement department, defining and implementing the Group Responsible and Strategic Procurement data strategy, analytics capabilities and data governance framework.
Responsibilities
Develop and articulate comprehensive data and analytics vision and strategy aligned with the overall business objectives
Identify, design and prioritize key business questions that can be answered through data analysis, driving value creation
Champion a data-driven culture across the organization, promoting data literacy and the use of analytics for decision-making
Stay abreast of industry trends, emerging technologies, and best practices in data science, machine learning, and business intelligence
Act as a strategic advisor to Responsible and Strategic Procurement Leadership, utilizing data-driven insights to identify cost-savings opportunities, mitigate supply chain risks, and drive value creation
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Mathematics, Economics, or a related quantitative field
Lead and execute Canonical’s security risk management strategy, develop playbooks, conduct risk assessments, and collaborate cross‑functionally to strengthen product security.
Responsibilities
Define Canonical's security risk management standards and playbooks
Analyse and improve Canonical's security risk practices
Evaluate, select and implement new security requirements, tools and practices
Grow the presence and thought leadership of Canonical security risk management practice
Develop Canonical security risk learning and development materials
Lead technical development of structured finance credit models and provide hands‑on leadership to the Quant team.
Compensation
EUR 128,500.00-186,833.00
Responsibilities
Take hands‑on ownership of the design, implementation and enhancement of structured finance credit models.
Lead complex model and methodology development projects through to release, coordinating technical workstreams, dependencies, timelines and stakeholders across quantitative, ratings, review and Technology teams.
Develop, maintain and enhance proprietary Python and C++ modelling libraries.
Engage with rating analysts to understand analytical challenges and emerging market trends, and translate business needs into appropriate modelling approaches.
Lead the preparation of quantitative research papers and technical documentation supporting model development and methodology enhancements.
Requirements
Master’s degree in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or a related quantitative discipline
As an intern in Sustainability Data Solutions & AI, you will turn sustainability data into insight Novo can act on.
Compensation
If allowed by your educational institution, the internship is paid at a fixed monthly base rate of DKK 15,900, plus a pension scheme (currently 10% employer contribution and 5% employee contribution).
Responsibilities
Develop a data analytics use case that combines energy and water data from source systems with global sustainability data
Support the integration and automation of sustainability data in the Sustainability Data Foundation
Improve the quality, transparency and availability of sustainability data by creating clean, reliable and reusable datasets
Support the digitalisation of sustainability data and analytics by connecting sustainability information with relevant business and operational metrics
Contribute to BI solutions for reporting, forecasting and decision-making, as well as ad hoc analyses across sustainability data and technology
Develop and deploy models for marketing effectiveness, acquisition, retention and customer lifetime value
Define analytical questions, hypotheses and business-linked success metrics, and design experiments and apply causal inference, MMM or attribution where appropriate
Reconcile web, advertising-platform, CRM and sales data into trusted analytical datasets
Communicate results, uncertainty and recommendations clearly to decision-makers
Embed analytical outputs into campaign, planning, budgeting and CRM workflows
Requirements
Degree in data science, statistics, mathematics, economics, econometrics, computer science or a related quantitative discipline
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