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