Senior Real-Time ML Scientist (Remote, AU)

Jobgether SRL; Australia; Remote; AUD 180,000 - 240,000; Full time

Benefits offered by this job

Fully remote

Job summary

Jobgether is seeking a Senior Data Scientist based in Australia for a fully remote, hands-on role focusing on real-time browser and device identification. You will lead end-to-end ML initiatives, from research and experimentation to production deployment, collaborating with data science and engineering teams across a distributed environment.

The role emphasizes strong ML fundamentals, real-time services, and a culture of data-driven decision making, with opportunities to shape engineering

Qualifications

• 5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines.
• Advanced knowledge of machine learning fundamentals and statistical methodologies.
• Strong practical experience with supervised learning, including gradient boosting and approaches for high-cardinality categorical data.
• Hands-on experience with semi-supervised and unsupervised learning techniques.
• Strong exploratory data analysis and creative problem-solving skills, particularly when working with incomplete, noisy, or unlabeled datasets.
• Proven experience developing real-time machine learning services, including model inference and integration between models and production applications.
• Ability to transform machine learning models into minimum viable real-time web services and production-ready solutions.
• Excellent coding and software engineering skills, including strong SQL capabilities and familiarity with Git, CI/CD pipelines, IDEs, and shell scripting.
• Strong communication skills and fluent English, with the ability to collaborate effectively in a distributed international environment.
• A research mindset and academic background are advantageous.
• Experience with Go and backend development is a plus.
• Familiarity with analytical data platforms such as ClickHouse, Snowflake, or BigQuery is beneficial.
• Experience with data transformation frameworks such as dbt, visualization tools such as Superset, Tableau, or Looker, or vector databases such as Pinecone, FAISS, or Qdrant is a plus.
• Experience building embedding-based search systems is advantageous.
• Familiarity with technologies such as Go, SQL, advanced ML frameworks, ClickHouse, dbt, and AWS is beneficial.

Responsibilities

• Develop data-driven algorithms using raw, noisy, and unlabeled data to improve browser and device identification capabilities.
• Design and implement supervised, semi-supervised, and unsupervised machine learning approaches, including methods for high-cardinality categorical data.
• Own data science initiatives end to end, from problem definition and experimentation through production deployment and integration with real-time services.
• Design experiments and technical solutions for real-time inference, model-to-service integration, training automation, and other machine learning engineering challenges.
• Conduct exploratory data analysis to investigate business and technical questions, identify anomalies, and evaluate model and dataset performance.
• Develop practical approaches for collecting and evaluating data when labeled datasets are limited or unavailable.
• Contribute to an engineering-focused, data-driven culture by sharing tools, methodologies, and effective data science practices with colleagues.
• Collaborate across data science and engineering functions to turn machine learning concepts into reliable, production-ready services.
• Participate in a shared on-call rotation, with schedules communicated in advance and coverage designed to minimize disruption.

Skills

Machine learning

Data science

Backend development

SQL

Go

CI/CD

Git

Python

Cloud (AWS)

Tools

ClickHouse

Snowflake

BigQuery

dbt

Superset

Tableau

Looker

Pinecone

FAISS

Qdrant

AWS

Job description

Jobgether is seeking a Senior Data Scientist based in Australia for a fully remote, hands-on role focusing on real-time browser and device identification. You will lead end-to-end ML initiatives, from research and experimentation to production deployment, collaborating with data science and engineering teams across a distributed environment.

The role emphasizes strong ML fundamentals, real-time services, and a culture of data-driven decision making, with opportunities to shape engineering