
Machine Learning Engineer | $77/hr Remote
Department:HR
Type:REMOTE
Region:Australia
Location:Australia
Experience:Associate
Salary:A$116,480 - A$160,160
Skills:
PYTHONPANDASNUMPYPOLARSSCIKITLEARNSQLMACHINELEARNINGDATASCIENCESTATISTICSFEATUREENGINEERINGNLPTIMESERIESKAGGLE
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Job Description
Posted on: February 15, 2026
Position: Data Scientist (Kaggle Grandmaster)
Type: Hourly Contract (Independent Contractor)
Compensation: $56 – $77/hour
Location: Remote
Commitment: 30–40 hours/week (flexible; full-time optional)
Role Responsibilities
- Analyze large, complex datasets to uncover patterns, generate insights, and inform modeling strategies.
- Build end-to-end predictive models, statistical analyses, and machine learning pipelines across tabular, time-series, NLP, and multimodal datasets.
- Design and implement robust validation strategies, experimentation frameworks, and analytical methodologies.
- Develop automated data workflows, feature engineering pipelines, and reproducible research environments.
- Conduct exploratory data analysis, hypothesis testing, and model-driven investigations to support research and product teams.
- Translate analytical and modeling outcomes into clear, actionable recommendations for engineering, product, and leadership stakeholders.
- Collaborate with machine learning engineers to productionize models and ensure data workflows operate reliably at scale.
- Create structured dashboards, reports, and documentation to clearly communicate findings.
- Support high-impact research and product initiatives through advanced analytical problem-solving.
Requirements
- Kaggle Competitions Grandmaster or equivalent demonstrated excellence (top-tier rankings, multiple medals, or exceptional competition performance).
- Strong professional experience in data science or applied analytics.
- Strong proficiency in Python and data science libraries such as Pandas, NumPy, Polars, and scikit-learn.
- Hands-on experience building machine learning models end-to-end, including feature engineering, training, evaluation, and deployment.
- Solid understanding of statistical methods, experiment design, and causal or quasi-experimental analysis.
- Experience working with modern data stacks, including SQL, distributed datasets, dashboards, and experiment tracking tools.
- Excellent communication skills with the ability to clearly present complex analytical insights.
- Ability to work independently in a remote, fast-paced research environment.
- Strong analytical thinking and problem-solving skills with attention to detail.
Application Process
- Upload resume (Kaggle profile required)
- Interview (15–30 min)
- Submit form
Originally posted on LinkedIn
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