Lead Data Scientist

About VERSA AI

VERSA AI is an innovative and fast-growing start-up in the Contact Centre automation space designing and deploying GenAI voice and chat agents. We’re on a mission to build and support the Contact Centre of the future, using the power of conversational AI to create natural, beautiful conversations between humans and machines.

Our technology helps clients deliver exceptional customer experiences while simultaneously cutting costs and scaling efficiently. An Australian-founded business with a strong foothold in the US, we are expanding our operations and building out our team on the ground in the critical market.

We are looking for passionate, entrepreneurial individuals who are eager to make a significant impact in a dynamic, fast-moving environment, and who want to shape how we deliver our platform and inform what we build next through real world implementations.

The Role

We're hiring a Lead Data Scientist who builds, not just advises. You'll own our data science function end-to-end, building models, engineering pipelines, creating insights, and turning the rich data generated by our AI platform into competitive advantage.

This isn't a strategy-only seat. You'll design the approach and do the work. Reporting to the Head of Engineering, your scope covers the entire business: product, engineering, customer success, operations, finance, and people not just the profit centres.

You'll be our first dedicated data science hire, with the opportunity to shape and grow a team as the function matures.

What You'll Do

• Build and deploy ML models and statistical analyses that solve real business problems, from customer behaviour prediction to cost optimisation
• Work hands-on with LLM and RAG system data: retrieval quality, conversation outcomes, latency, and model performance
• Architect scalable data pipelines across cloud platforms (AWS/GCP/Azure) and stand up a modern data stack
• Create dashboards and operational scorecards that teams across the business actually use
• Define key metrics: customer experience, product reliability, unit economics, and operational health
• Establish data governance and compliance frameworks (Australian Privacy Act, GDPR, SOC 2)
• Translate ambiguous business questions into measurable data problems for non-technical stakeholders
• Build the case for growing the data function and hire your team as the need arises

What You Bring

• 8–10+ years across data science, data engineering, analytics, or applied AI/ML
• Track record of deploying ML models in production, not just prototyping in notebooks
• Strong Python, SQL, and modern data tooling (dbt, Airflow/Dagster, Spark, Snowflake/BigQuery)
• Experience with LLM-based systems: RAG architectures, vector databases, orchestration frameworks
• Solid statistical foundations: experimental design, causal inference, regression, time-series
• Ability to communicate data insights to senior stakeholders and non-technical audiences
• Experience in SaaS, AI/ML product companies, or enterprise implementation environments

Bonus Points

• Familiarity with conversational AI metrics (deflection, containment, intent accuracy)
• MLOps experience: model versioning, monitoring, CI/CD for ML
• Australian market experience and familiarity with local privacy legislation
• Quantitative degree (Master's/PhD valued, not required if experience is strong)