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Remote Data Scientist (Kaggle-Grandmaster) - AI Trainer ($56-$77 per hour)
Remote Data Scientist (Kaggle-Grandmaster) - AI Trainer ($56-$77 per hour)Mercor • Allen, Texas, US
Remote Data Scientist (Kaggle-Grandmaster) - AI Trainer ($56-$77 per hour)

Remote Data Scientist (Kaggle-Grandmaster) - AI Trainer ($56-$77 per hour)

Mercor • Allen, Texas, US
[job_card.variable_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
  • [filters.remote]
[job_card.job_description]

##

  • Role Description

Mercor is hiring on behalf of a leading AI research lab to bring on a highly skilled

  • Data Scientist
  • with a
  • Kaggle Grandmaster profile.
  • In this role, you will transform complex datasets into actionable insights, high-performing models, and scalable analytical workflows. You will work closely with researchers and engineers to design rigorous experiments, build advanced statistical and ML models, and develop data-driven frameworks to support product and research decisions.
  • ##
  • What You’ll Do
  • Analyze large, complex datasets to uncover patterns, develop insights, and inform modeling direction - Build predictive models, statistical analyses, and machine learning pipelines across tabular, time-series, NLP, or multimodal data - Design and implement robust validation strategies, experiment frameworks, and analytical methodologies - Develop automated data workflows, feature pipelines, and reproducible research environments - Conduct exploratory data analysis (EDA), hypothesis testing, and model-driven investigations to support research and product teams - Translate modeling outcomes into clear recommendations for engineering, product, and leadership teams - Collaborate with ML engineers to productionize models and ensure data workflows operate reliably at scale - Present findings through well-structured dashboards, reports, and documentation
  • ##
  • Qualifications
  • Kaggle Competitions
  • Grandmaster
  • or comparable achievement : top-tier rankings, multiple medals, or exceptional competition performance - 3–5+ years of experience in data science or applied analytics - Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) - Experience building ML models end-to-end : feature engineering, training, evaluation, and deployment - Solid understanding of statistical methods, experiment design, and causal or quasi-experimental analysis - Familiarity with modern data stacks : SQL, distributed datasets, dashboards, and experiment tracking tools - Excellent communication skills with the ability to clearly present analytical insights
  • ##
  • Nice to Have
  • Strong contributions across multiple Kaggle tracks (Notebooks, Datasets, Discussions, Code) - Experience in an AI lab, fintech, product analytics, or ML-focused organization - Knowledge of LLMs, embeddings, and modern ML techniques for text, images, and multimodal data - Experience working with big data ecosystems (Spark, Ray, Snowflake, BigQuery, etc.) - Familiarity with statistical modeling frameworks such as Bayesian methods or probabilistic programming
  • ##
  • Why Join
  • Gain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation analytical systems. - Work on high-impact data science challenges while experimenting with advanced modeling strategies, new analytical methods, and competition-grade validation techniques. - Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal analytics. - Flexible engagement options (30-40 hrs / week or full-time) — ideal for data scientists eager to apply Kaggle-level problem-solving to real-world, production analytics. - Fully remote and globally flexible work structure — optimized for deep analytical work, async collaboration, and high-output research.
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