Senior Data Scientist

Position Overview

We are collaborating with a forward-thinking technology organization based in Sydney to recruit a Senior Data Scientist. This pivotal role demands an experienced expert with a solid quantitative background and a demonstrated ability to tackle complex measurement and forecasting challenges implemented in production systems. Reporting directly to senior leadership, you will oversee the entire model lifecycle — from designing solutions and translating unclear business issues into statistical models, to deploying reliable models and presenting key insights to executives.

Key Responsibilities

• Design and lead the development and deployment of reliable statistical and machine learning models aimed at forecasting demand and key business metrics using methods such as regularized regression and gradient boosting, while ensuring interpretability through tools like SHAP.
• Lead causal inference initiatives including incrementality measurement strategy utilizing complex experimental designs, advanced A/B testing frameworks, matching techniques, and synthetic controls.
• Develop and scale advanced forecasting models, particularly Bayesian Structural Time Series (BSTS), to assess incremental impact and predict against established baselines.
• Collaborate closely with business leaders to turn ambiguous targets into precise, quantifiable analytical projects.
• Produce production-grade, reproducible analysis code, promoting data science best practices including version control, CI/CD, and MLOps processes across the team.
• Effectively communicate intricate statistical concepts and their business implications to non-technical stakeholders and executives.
• Provide mentorship and guidance to junior team members focusing on statistical rigor, coding standards, and practical solution development.

Candidate Profile and Requirements

• Minimum of 5 years industry experience as a data scientist with a strong emphasis on forecasting, causal inference, and machine learning techniques.
• Educational credentials in quantitative disciplines such as Data Science, Statistics, Mathematics, Computer Science, or Econometrics; possession of a Master's or PhD degree is highly advantageous.
• Expert-level understanding of statistics and probability including uncertainty quantification, experimental design, and hypothesis testing in noisy real-world contexts.
• Advanced programming skills in Python and SQL to manage complex data transformations, modeling, and deployment tasks.
• In-depth knowledge of MLOps principles and engineering practices related to model deployment, drift monitoring, and lifecycle management.
• Excellent communication skills to clearly articulate complex methodologies and their limitations to non-technical audiences.
• Preferred qualifications include hands-on experience with advanced causal inference methods, econometrics, Bayesian time-series forecasting methods, and practical application in production environments.
• Experience working with modern data infrastructure such as Snowflake, dbt, cloud platforms including AWS, GCP, or Azure, and workflow orchestration tools is beneficial.
• Proven ability to lead or expand a data science team within dynamic, technology-driven or B2B SaaS businesses is an asset.

What the Company Provides

• Opportunity to own critical technical responsibilities that directly influence company strategy and impact.
• Leadership roles enabling influence over the technical direction of the data science team and development of junior colleagues.
• Work environment composed of highly skilled data engineers and scientists within a mature data infrastructure.
• Attractive compensation package, including a competitive salary, budget for continuous learning and development, flexible/hybrid working options, and ample leave benefits.