Data Scientist

• Lead the design, development and implementation of machine learning, Generative AI and agentic AI solutions for strategic business and customer outcomes
• Apply statistical methods and structured problem solving to complex fraud and scams challenges
• Progress solutions from exploration and experimentation through evaluation, validation, deployment and ongoing monitoring
• Design and develop Generative AI capabilities, including Retrieval-Augmented Generation, orchestration, guardrails and agentic AI systems
• Identify opportunities to expand the effective and responsible use of data science, machine learning and AI across fraud and scams
• Partner with product, engineering, platform, architecture, MLOps, risk and business stakeholders
• Define problems, evaluate trade-offs and translate technical insights into action
• Establish technical practices across data and feature engineering, model selection, evaluation, testing, documentation and production deployment
• Lead and develop the chapter through coaching, knowledge sharing, technical guidance and collaboration

Chapter Lead, Data Science – Fraud, Scams & Cyber

• Establish and lead CES's Data & Architecture function
• Define and lead CES's enterprise Data & Architecture strategy
• Establish governance, ownership and decision-making frameworks across data, architecture and technology strategy
• Mature data governance, data quality, master data management and enterprise data practices
• Shape the future direction of data platforms and engineering capability
• Establish enterprise architecture principles, standards and target-state roadmaps
• Guide strategic technology decisions across platforms, integrations, APIs, data services and emerging technologies, including AI opportunities
• Build partnerships across Technology and the broader business
• Translate complex technical concepts into clear business outcomes and recommendations
• Develop the operating model, team capability and future organisational structure for the Data & Architecture function
• Report to the Chief Technology Officer

Head of Data – Architecture

• Translate company-level objectives into strategic sub-objectives across analytics, ML, AI products, and experimentation
• Lead, mentor, and grow a team of data scientists, analytics engineers, ML engineers, and managers from approximately 12 to 30 people
• Design team structures and operating models for high throughput across multiple locations and time zones
• Drive AI-native ways of working and shipping value across Xero
• Act as the primary data partner to Product & Technology
• Advise C-suite and senior leadership on data utilization, retention strategy, new data domains, and AI investment decisions
• Represent data science in executive forums
• Partner with Product, Engineering, Design, Marketing, Commercial, Legal, Privacy, and Risk on strategy, prioritization, and governance
• Own AI governance, model risk, data quality, privacy posture, and experimentation integrity across the portfolio
• Drive behavioral, technical, and cultural standards across the vertical
• Improve global hiring practices and build talent maps
• Coach senior leaders and individual contributors
• Identify delivery and strategic risks and design organizational processes for consistent, high-quality outcomes

General Manager, Data Science

• Own a real-world project stream end-to-end, such as syndicate detection, user behavior sequencing, market manipulation detection, or feature discovery
• Process and analyze billions of records using PySpark, Hive, and Trino
• Develop robust SQL pipelines for feature engineering and validation
• Build, evaluate, and iterate on machine learning models
• Translate model performance into business metrics such as detection rate, false-positive rate, and latency
• Work with the Risk AI DS team on risk-detection models using large-scale user behavior, fund-flow, and market data

Data Scientist, Analytics

• Lead end-to-end delivery of data programmes and the broader data roadmap across multiple squads
• Own complex, multi-workstream programmes from initiation through closure, including plans, milestones, governance, budgets, RAID management and benefits realisation
• Drive Agile and DataOps practices across sprint planning, backlog management, estimation, capacity planning and dependency management
• Manage critical dependencies across source systems, data providers and engineering teams
• Anticipate delivery risks including upstream changes, outages, schema drift and late or poor-quality data, developing contingency plans
• Provide delivery reporting to stakeholders, leadership and governance forums using delivery KPIs and transparent risk escalation
• Partner with Product, Trading, Risk, Finance and Marketing to translate business goals into achievable plans and negotiate priorities
• Manage demand intake and governance to ensure work is defined, prioritised and resourced
• Embed governance, security and regulatory requirements into the delivery lifecycle
• Set delivery standards for quality, documentation and audit readiness
• Manage vendor and partner relationships supporting data platform tooling and delivery
• Build cross-functional relationships and influence outcomes without direct authority

Delivery Lead – Data

• Deliver cutting-edge AI/ML capabilities and tools in a project-based capacity to accelerate key business objectives, including cost savings and revenue generation.
• Develop advanced AI solutions, including time series forecasting, reinforcement learning, optimization and optimal decision-making, generative AI, speech and conversational AI, and Graph AI.
• Champion the exploration and implementation of advanced AI techniques to address critical business challenges and stay abreast of emerging trends and technologies.
• Serve as a key contributor to the Foundation Model Task Force, driving the development and implementation of foundational AI models leveraged across the organization.
• Ensure AI initiatives adhere to standards of trust, transparency, safety, and security, fostering ethical AI practices and regulatory compliance.
• Develop and implement AI strategies aligned with business objectives, delivering improved efficiency, cost reductions, and new revenue streams.
• Collaborate with business leaders, technical experts, and cross-functional teams to integrate AI/ML initiatives with broader business strategies.
• Drive continuous improvement by incorporating emerging AI technologies and methodologies.
• Establish and manage performance metrics for AI/ML projects, ensuring timely, in-scope delivery and desired business outcomes.
• Report to the Head of AI Science and serve as a strategic advisor and custodian of institutional AI knowledge.

AI Data Science, Machine Learning Engineer

• Lead complex Data & AI consulting engagements from strategy and solution design through engineering, implementation and production deployment
• Advise senior technology and business leaders on Data & AI strategy, architecture, platforms, operating models and technology choices
• Shape end-to-end Data & AI solutions across data engineering, modern data platforms, analytics, machine learning, Generative AI, agentic AI and enterprise integration
• Provide technical leadership across solution architecture and engineering, ensuring scalable, secure and reliable enterprise solutions
• Lead productionisation of AI solutions, including MLOps/LLMOps, evaluation, monitoring, governance and Responsible AI practices
• Translate business challenges into Data & AI opportunities, roadmaps and measurable-value solutions
• Lead multidisciplinary teams of architects, data engineers, AI engineers, data scientists and consultants
• Build trusted relationships with senior client stakeholders and act as a recognised advisor on emerging Data & AI technologies
• Support business development by identifying opportunities, shaping propositions, developing proposals and contributing to client pursuits
• Evolve PwC’s Data & AI offerings, accelerators, methods and engineering capabilities
• Contribute to market presence through thought leadership, executive discussions, demonstrations and industry engagement

Data Scientist jobs in Australia