Data Scientist

Role overview

Use product data, experimentation, and applied modeling to improve a self-serve SaaS experience involving quiz creation and participation. This product-minded role spans metric design, behavioral analysis, model development, and communication of evidence-based recommendations while maintaining strong standards for privacy and analytical rigor.

Responsibilities

- Define product, acquisition, engagement, and retention metrics with explicit assumptions and limitations. - Analyze creation, participation, conversion, and customer-lifecycle behavior to explain product performance and identify opportunities. - Design and evaluate experiments, including statistical power, success criteria, guardrails, and post-launch analysis. - Build reproducible datasets, pipelines, and data-quality checks for analysis and modeling. - Develop and assess models for recommendations, classification, forecasting, anomaly detection, or other product applications. - Partner with engineering and marketing to turn findings into practical decisions. - Explain methodology, uncertainty, privacy considerations, and recommendations to technical and non-technical audiences.

Requirements

- At least three years of professional experience in data science, product analytics, applied statistics, or a related field. - Strong SQL and Python skills, with experience creating reproducible analyses and maintainable workflows. - Solid grounding in statistics, experimental design, causal reasoning, and model evaluation. - Experience with event, behavioral, or SaaS product data and translating business questions into measurable hypotheses. - Ability to identify data-quality problems, bias, and leakage without overstating conclusions. - Strong written and verbal communication, including clear visualizations and concise recommendations. - Independent working style and clear English communication in a remote environment. - Quantitative degree or equivalent practical experience.

Nice to have

- Experience with recommendation systems, natural language processing, or generative AI evaluation. - Familiarity with MongoDB, subscription products, self-serve funnels, or customer-generated content. - Experience deploying models or partnering with engineers on production data products. - Knowledge of privacy-aware analytics and responsible data use.

Benefits and work setup

- Full-time remote position open to candidates in Australia or California. - 38 hours per week, with flexible scheduling and reasonable overlap with a Melbourne-based team. - Salary listed in the source: USD 150,000 per year.