Senior Data Scientist

Role Overview

This opportunity is for a Senior Data Scientist based in Australia, working remotely, focused on enhancing the precision and intelligence of a high-volume, real-time identification platform. This hands-on position requires developing machine learning methods to accurately distinguish browsers and devices while dealing with complex, noisy, and frequently unlabeled data sets. The role integrates advanced machine learning, statistical analysis, experimentation, and backend engineering in a technically demanding environment, with ownership of projects from research to deployment and real-time service integration. Additionally, the candidate will influence data-driven and engineering best practices across the wider team.

Key Responsibilities

• Create data-driven algorithms that handle raw, noisy, and unlabeled data to boost browser and device recognition capabilities.
• Design, develop, and implement machine learning models including supervised, semi-supervised, and unsupervised techniques, especially addressing high-cardinality categorical variables.
• Manage data science projects end-to-end, from identification and experimentation through to deployment and operational integration.
• Devise experiments and technical strategies to tackle real-time inference, model-to-service integrations, and training automation challenges.
• Perform exploratory data analyses to address business and technical inquiries, identify anomalies, and assess model and dataset effectiveness.
• Innovate practical methods for collecting and evaluating data where labeled data limitations exist.
• Support and advance a culture of engineering-focused, data-driven methodologies by sharing tools and best practices.
• Collaborate across teams to translate machine learning concepts into reliable, production-ready services.
• Engage in a shared on-call rotation with scheduled notifications aiming to minimize disruptions outside standard working hours.

Required Qualifications & Experience

• Minimum of 5 years professional experience in machine learning, data science, backend development, or related engineering fields.
• In-depth knowledge of machine learning principles and statistical methods.
• Practical expertise in supervised learning, including gradient boosting and high-cardinality categorical data modeling.
• Hands-on experience with semi-supervised and unsupervised learning techniques.
• Strong skills in exploratory data analysis and problem-solving, especially with incomplete or noisy datasets.
• Demonstrated success in building and deploying real-time machine learning services with model inference and production integration.
• Ability to convert machine learning models into minimum viable real-time web services and production-ready systems.
• Excellent programming and software engineering capabilities, including proficient SQL, experience with Git, CI/CD pipelines, development environments, and shell scripting.
• Fluent English communication skills with demonstrated ability to work effectively in distributed international teams.
• Research-oriented mindset and academic background preferred.
• Experience in Go programming and backend development is advantageous.
• Knowledge of analytical platforms like ClickHouse, Snowflake, or BigQuery is beneficial.
• Familiarity with data transformation tools (e.g., dbt), data visualization platforms (e.g., Superset, Tableau, Looker), or vector databases (e.g., Pinecone, FAISS, Qdrant) is a plus.
• Experience developing embedding-based search systems is a further plus.
• Technical familiarity with Go, SQL, advanced ML frameworks, ClickHouse, dbt, and AWS cloud services is beneficial.

Benefits & Additional Information

• Competitive salary range approximately equivalent to USD 152,000–205,000 annually, subject to location-specific variation.
• Fully remote work setup supporting geographic flexibility.
• Engagement with challenging ML projects including real-time systems and large-scale data processing.
• Exposure to cutting-edge machine learning, data engineering, backend development, and cloud infrastructure technologies.
• Influence on technical strategy and contributions to engineering and data science processes.
• Collaboration with a globally diverse, distributed team promoting inclusivity.
• Applicants must be authorized to work from their place of residence; visa sponsorship is not available.
• Participation in scheduled shared on-call duties aiming to equitably distribute responsibilities and minimize disruption beyond typical hours.

Additional Information

Applications and further hiring procedures are managed exclusively by a partner company. Candidate selection involves an AI-powered matching system to ensure objective and efficient review against core criteria. Final hiring decisions rest with the partner company. The partner adheres to data privacy considerations including GDPR compliance. AI tools may support aspects of application review, but human judgment governs final employment decisions.