EL1 Manager Delivery

Softtest pays pty ltd; Sydney; On-site; AUD 140,000 - 190,000; Full time

Job summary

Softtest pays pty ltd seeks an EL1 Delivery Lead to coordinate and deliver data science, AI, and analytics initiatives across the DaSH. You will work with Data Scientists, AI engineers and business stakeholders to shape delivery plans, resolve dependencies, and ensure governance-compliant progression from problem to practical solution.

You will lead agile ceremonies, manage backlogs, and provide clear executive-facing updates.

Qualifications

• Experience leading data science or AI-enabled delivery projects.
• Ability to coordinate cross-functional teams and maintain priorities.
• Familiarity with governance, reporting, and executive communications.
• Strong stakeholder engagement across multiple squads and senior leadership.

Responsibilities

• Facilitate delivery across DaSH squads.
• Coordinate delivery plans, dependencies, risks and decisions.
• Collaborate with EL1 Lead Data Scientists and AI/Engineering leads.
• Support discovery, scoping, POC planning and handover to operations.
• Prepare status updates, governance artefacts and executive delivery reports.

Skills

Delivery leadership

Stakeholder management

Agile delivery

Data science familiarity

Job description

Australian Citizens residing in Australia only respond.

Job Details:
Role/s: 1 x EL1 Delivery Lead
The EL1 Delivery Lead, Data Science Hub (DaSH) is accountable under broad direction for facilitating and coordinating the delivery of data science, machine learning, artificial intelligence and advanced analytics initiatives across the Data Science Hub.

The EL1 Delivery Lead, Data Science & AI Engineering Lead, Business Analysts, Data Scientists, AI Engineers and business stakeholders to support the successful delivery of complex initiatives. The Delivery Lead does not replace or override the technical leadership of Lead Data Scientists. Rather, the role enables delivery by helping teams define the work, organise the delivery approach, manage dependencies, facilitate ceremonies, coordinate governance and keep stakeholders aligned.

The EL1 Delivery Lead supports the Portfolio Director, Data Science to manage the DaSH delivery portfolio, including scoping, prioritisation, proof of concept planning, delivery reporting, governance artefacts, business papers and senior executive submissions. The role helps ensure that DaSH initiatives are clearly framed, appropriately governed, and supported by the right delivery practices to move from business problem to practical solution.

The role requires strong collaboration across multiple squads and business areas. It is expected to work closely with peer EL1 Lead Data Scientists and AI & Engineering delivery leads to ensure delivery plans reflect both the business problem being solved and the technical realities of AI and data science delivery.

The Delivery Lead facilitates and coordinates delivery in partnership with Lead Data Scientists and AI & Engineering leads; technical leadership for analytical methods, model design and solution quality remains with the relevant Lead Data Scientists and technical leads.

Key duties and responsibilities

Delivery facilitation across DaSH squads

• Facilitate the delivery of multiple data science, AI, machine learning and advanced analytics initiatives across DaSH, supporting several teams or squads at any one time.
• Coordinate delivery activities across multidisciplinary squads, ensuring teams have clear priorities, delivery plans, dependencies, risks and decision points.
• Work collaboratively with EL1 Lead Data Scientists and the Data Science & AI Engineering Lead to support delivery of technically sound and business-aligned AI and data science solutions.
• Support teams to move from discovery and scoping through to proof of concept, pilot, implementation planning and operational handover, where appropriate.
• Maintain visibility of delivery progress, risks, assumptions, issues, dependencies and decisions using fit-for-purpose delivery tools and reporting mechanisms.
• Help build a productive delivery environment where team members understand their role, remain accountable for their contribution, and work together to deliver practical outcomes.

Partnership with Lead Data Scientists and technical leads

• Work in conjunction with EL1 Lead Data Scientists to ensure delivery planning reflects technical feasibility, analytical complexity, data availability, model development cycles and evaluation requirements.
• Recognise and support the central role of Lead Data Scientists in leading the analytical approach, model design, technical quality and scientific rigour of DaSH solutions.
• Coordinate delivery activities so that Lead Data Scientists and technical specialists can focus on solution design, modelling, experimentation, evaluation and technical leadership.
• Facilitate alignment between delivery priorities, business expectations and the technical direction set by Lead Data Scientists and AI & Engineering leads.
• Support cross-squad collaboration by helping identify common delivery issues, shared dependencies, resourcing constraints and opportunities for reuse.

Business problem discovery and scoping

• Lead and guide Business Analysts, data scientists and stakeholders to identify the why and what of complex business problems that DaSH is asked to solve.
• Support Business Analysts to clarify business needs, user problems, desired outcomes, success measures, constraints and decision points.
• Work with Business Analysts, Lead Data Scientists and business stakeholders to determine whether a proposed problem is suitable for a data science, AI, machine learning or analytics-based response.
• Facilitate workshops and discovery sessions to help business areas articulate the problem, understand solution options, and identify the value proposition for DaSH involvement.
• Ensure problem statements, scope documents and proof of concept proposals are sufficiently clear to support technical assessment, prioritisation and executive decision-making.

Agile and fit-for-purpose delivery practices

• Facilitate sprint ceremonies, planning sessions, showcases, retrospectives, backlog refinement and delivery check-ins as appropriate to the nature of each initiative.
• Tailor delivery practices to suit AI and data science work, recognising that research, experimentation, data access, model evaluation and stakeholder validation may not always follow standard ICT delivery patterns.
• Support Lead Data Scientists, Business Analysts and AI & Engineering leads to maintain prioritised backlogs that reflect business value, feasibility, technical dependencies and delivery constraints.
• Coordinate sprint and release planning where useful, while allowing flexibility for exploratory analysis, model iteration and proof of concept development.
• Promote continuous improvement in DaSH ways of working, including better delivery cadence, clearer prioritisation, improved documentation and more consistent stakeholder communication.

Governance, assurance and executive support

• Assist the Portfolio Director, Data Science to prepare scoping documents, proof of concept proposals, delivery plans, business cases, governance artefacts and business papers for senior executive review and approval.
• Coordinate input from Lead Data Scientists, AI & Engineering leads, Business Analysts, governance teams and business stakeholders into executive-level documentation.
• Support alignment with Agency governance, privacy, cyber security, data access, responsible AI, risk and assurance requirements.
• Track governance checkpoints, stage gates, approvals, risks and issues across the DaSH portfolio.
• Prepare concise status updates, sprint summaries, risk summaries and delivery reports for the Portfolio Director and relevant governance forums.

Stakeholder engagement and communication

• Build and maintain productive relationships with business stakeholders, technical teams, governance areas and senior executive offices.
• Act as a key delivery interface between DaSH squads and business areas, ensuring expectations are clear, decisions are documented and issues are escalated appropriately.
• Communicate delivery progress, risks, blockers and decisions in a clear and practical way for both technical and non-technical audiences.
• Support business areas to understand the iterative nature of AI and data science delivery, including the need for experimentation, evaluation and staged decision-making.
• Facilitate shared ownership of outcomes between business stakeholders, Lead Data Scientists, AI & Engineering leads and delivery teams.