Crossing Hurdles logo

Machine Learning Engineer | $77/hr Remote

Crossing Hurdles
Department:Data Analysis
Type:REMOTE
Region:EU
Location:France
Experience:Associate
Salary:$116,480 - $160,160
Skills:
PYTHONPANDASNUMPYPOLARSSCIKITLEARNMACHINELEARNINGDATASCIENCESTATISTICSSQLDATABASESFEATUREENGINEERINGMODELEVALUATIONEXPERIMENTDESIGNNLPTIMESERIESKAGGLE
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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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