Lead Data Scientist We are hiring a Lead Data Scientist to own and build the data science function from the ground up. This is a hands-on, high-impact role focused on turning complex product, customer and operational data into measurable business value. This is not a strategy-only position. You will design the approach, build the models, engineer pipelines, create insights, and establish the data practices that support a growing AI-powered platform. You will work closely with engineering, product, customer success, operations, finance and leadership to define what data means across the business and how it can drive better decisions. About the role As the first dedicated data science hire, you will have the opportunity to shape the company’s data strategy, build the foundations of a modern data stack, and create the models, dashboards and reporting frameworks that teams rely on. You will work across machine learning, applied AI, data engineering, analytics, governance and business intelligence. The role is suited to someone who is highly technical, commercially minded and comfortable operating in a fast-moving environment where they can build from scratch. Key responsibilities Design, build and deploy machine learning models and statistical analyses that solve real business problems Work hands-on with LLM and RAG system data, including retrieval quality, conversation outcomes, latency and model performance Build experimentation frameworks, including A/B testing and causal inference, to validate product and business decisions Develop NLP and text analytics capability to extract insights from conversation data at scale Architect scalable data pipelines across cloud platforms such as AWS, GCP or Azure Build and maintain a modern data stack across ingestion, transformation, warehousing, orchestration and data quality Establish data governance practices including cataloguing, lineage, access controls and compliance frameworks Create dashboards, scorecards and executive reporting across product, engineering, customer success, finance, operations and leadership Define key metrics across customer experience, product reliability, unit economics and operational health Translate ambiguous business questions into clear, measurable data problems Build the business case for growing the data function and hire or mentor future team members as needed What you will bring 8–10 years’ experience across data science, data engineering, analytics or applied AI/ML Proven experience building and deploying machine learning models in production Strong Python and SQL capability Experience with modern data tooling such as dbt, Airflow, Dagster, Spark, Snowflake, BigQuery or Redshift Experience architecting cloud-native data platforms across AWS, GCP or Azure Hands-on experience with LLM-based systems, RAG architectures, vector databases and orchestration frameworks Strong statistical foundations across experimental design, causal inference, regression, classification and time-series analysis Experience with data governance, privacy and compliance in enterprise or regulated environments Ability to communicate complex data insights to senior stakeholders and non-technical audiences Experience in SaaS, AI/ML product companies or enterprise implementation environments Nice to have Experience working in the Australian market and familiarity with Australian privacy legislation Familiarity with conversational AI metrics such as deflection, containment, intent accuracy and ASR/TTS performance MLOps experience across model versioning, monitoring and CI/CD for ML pipelines A quantitative degree in computer science, statistics, mathematics, physics, engineering or similar Cloud or data certifications such as AWS Solutions Architect or GCP Professional Data Engineer What success looks like In the first 30 days, you will complete a deep immersion into the product, data landscape and AI architecture, assess the current state of data, and deliver a clear roadmap covering risks, quick wins, metrics and priorities. Over the following months, you will strengthen core data platform components, launch operational scorecards, improve data quality and model performance, and establish reporting rhythms across the business. Over 6–12 months, you will have built a trusted data foundation, improved business outcomes through data science, matured metric ownership, and developed a clear plan for growing the data function. Benefits Join a fast-growing, innovative technology environment Build and lead a data function from the ground up Work at the forefront of AI, data science and conversational technology High level of ownership, autonomy and business impact Strong career growth and professional development opportunities Competitive salary Flexible, remote-first working environment
