2 days ago

Senior Data Scientist

carsales

Melbourne

2 days ago

Apply

Senior Data Scientist role at carsales in Melbourne, Australia, focused on delivering end-to-end data science solutions, including personalisation, recommendation, and ranking. Requires substantial hands-on experience in statistical modelling, machine learning, experimentation, and advanced programming, with a hybrid work model.

AI-enabledHybridFull-timeSeniorStatistical ModellingMachine Learning

Senior Data Scientist

Apply

AI-enabledHybridFull-timeSeniorStatistical Modelling

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Hybrid working, combining the flexibility of remote work with the energy and connection of in-person collaboration

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

Statistical ModellingMachine LearningA/B TestingAWSSageMakerSnowflakeDatabricksMLflowMetaflowPython

Soft Skills

CollaborationAnalytical CommunicationProblem FramingPeer ReviewCustomer ObsessedCuriousConnected

Preferred Qualifications

Technical (Nice-to-have)

Recommendation SystemsRankingPersonalisationTwo-Tower ArchitecturesSemantic EmbeddingsSequence ModellingCold-Start Strategies

Key Responsibilities

•Deliver end-to-end data science: Lead and contribute to data science initiatives from problem definition and exploratory analysis through to validated solutions, working collaboratively to turn complex customer and business problems into measurable outcomes.
•Turn data into insights: Analyse complex behavioural, automotive, and business data to identify meaningful patterns and opportunities, using clear visualisations and data storytelling to inform product and business decisions.
•Drive personalisation, recommendation and ranking: Develop models that improve customer intent understanding, relevance, and personalisation, including approaches for candidate generation, ranking, behavioural modelling, and cold-start scenarios.
•Own experimentation and impact measurement: Design rigorous offline and online evaluations, establish credible baselines and success measures, and use experiments such as A/B tests to determine whether solutions deliver genuine customer and commercial value.
•Champion analytical and modelling standards: Apply and uphold shared practices for problem framing, model development, evaluation, reproducibility, documentation, and governance so that data science work is consistent, traceable, and supported by clear evidence.
•Collaborate, communicate and influence: Work closely with colleagues across Tech, Data, Product, and Commercial teams to align priorities, communicate findings and trade-offs clearly, and support the adoption of delivered insights and solutions.

Data ScienceMachine LearningAIPersonalisationRecommendationRankingExperimentationAWSSnowflakeDatabricks

Key Responsibilities

•Deliver end-to-end data science: Lead and contribute to data science initiatives from problem definition and exploratory analysis through to validated solutions, working collaboratively to turn complex customer and business problems into measurable outcomes.

•Turn data into insights: Analyse complex behavioural, automotive, and business data to identify meaningful patterns and opportunities, using clear visualisations and data storytelling to inform product and business decisions.

•Drive personalisation, recommendation and ranking: Develop models that improve customer intent understanding, relevance, and personalisation, including approaches for candidate generation, ranking, behavioural modelling, and cold-start scenarios.

•Own experimentation and impact measurement: Design rigorous offline and online evaluations, establish credible baselines and success measures, and use experiments such as A/B tests to determine whether solutions deliver genuine customer and commercial value.

•Champion analytical and modelling standards: Apply and uphold shared practices for problem framing, model development, evaluation, reproducibility, documentation, and governance so that data science work is consistent, traceable, and supported by clear evidence.

•Collaborate, communicate and influence: Work closely with colleagues across Tech, Data, Product, and Commercial teams to align priorities, communicate findings and trade-offs clearly, and support the adoption of delivered insights and solutions.