About the role:
This is a player-coach role: do the science yourself, and own how it is done across the practice. As a Data Science Team Lead you will be accountable for the health of the data science practice at LMG, the standards people work to, the growth of the people in it, the quality of what leaves it, and the bar for who joins.

Your craft home is this team. Your delivery home is the initiative you are assigned to. Data Scientists at LMG are aligned to a domain and stay there long enough to build real depth and real relationships, but the craft team is the constant: it is where your standards, your review, your growth and your career sit.

Like everyone in the practice you are deployed into delivery initiatives, where you deliver to the expectations of the Principal Data Scientist. That is roughly 80% of your time. The other 20% is holding the practice together, and it is not optional.

You will hold the technical standard of a Principal Data Scientist. We are explicit that this role stays hands-on. If it stops being hands-on, something has gone wrong and we will fix it rather than quietly redefine the role.

This is the right role for a senior or principal data scientist who wants to shape how a discipline works without stepping away from practising it. Reporting to the GM Data and AI Enablement, this role leads a team of Data Scientists.

What you will do:

- Work hands-on as a data scientist to the standard expected of a Principal Data Scientist. Build domain knowledge and strong relationships with stakeholders.
- Set the standard for how we do data science: framing problems, designing experiments, selecting models, baselines and holdouts, uncertainty, documentation and monitoring applied consistently across a distributed team.




- Responsible AI isn't ours alone. Make LMG's standards practicable for broad adoption, and hold your own team to them.
- Set the bar for models in Production: What triggers a return to discovery - retrain, prompt and retrieval change, redesign etc, and what evidence declares a model fit for purpose
- Own key person risk across the team, a named owner, avoidance of single person risk, adoption of practices to ensure knowledge sharing
- Hold 1:1’s with your direct reports which includes having growth plan conversations, feedback and performance discussions. You have input into who is deployed where to support development goals.
- Equip your people to challenge and escalate when standards are missed, peer review work before it ships, know when to escalate.
- Own the technical hiring loop and new starters' first ninety days. Know where the practice is thin and build the capability before it is needed.

What you will bring:

- The technical depth to be the strongest scientist in the room without needing to be.
- Deep hands-on experience taking models from question to production and keeping them helpful, not just building them.
- Experience growing data scientists, including having feedback and development conversations.
- Judgement about evidence: what a result actually supports, and when a number is too weak to act on.
- Experience setting standards that people want to follow, without relying on authority to make it happen.
- Experience with responsible AI in a regulated setting: fairness, explainability, human oversight, or the appetite to learn on the go.
- A track record of work that changed a decision, not just work that was delivered.
- The instinct to notice when someone is finding it a challenge, including when they are deployed away from you.

📌 Data Science Lead (Melbourne)
🏢 Ray White
📍 Melbourne