Mercor
Data Scientist (Kaggle-Grandmaster)
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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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.
#J-18808-Ljbffr