Data Scientist - Kaggle Grandmaster

Role overview

Responsibilities

• check_circle Analyze large, complex datasets to uncover patterns and generate actionable insights
• check_circle Build predictive models and ML pipelines across: Tabular data Time-series data NLP Multimodal datasets
• check_circle Tabular data
• check_circle Time-series data
• check_circle NLP
• check_circle Multimodal datasets
• check_circle Design and implement validation strategies, experimental frameworks, and analytical methodologies
• check_circle Develop automated data workflows, feature pipelines, and reproducible research environments
• check_circle Conduct exploratory data analysis (EDA), hypothesis testing, and model-driven investigations
• check_circle Translate analytical results into clear recommendations for engineering, product, and leadership teams
• check_circle Collaborate with ML engineers to productionize models and ensure reliable data workflows at scale
• check_circle Present findings via dashboards, structured reports, and documentation

Basic 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, deployment)
• Strong understanding of statistical methods, experiment design, and causal/quasi-experimental analysis
• Familiarity with modern data stacks (SQL, distributed datasets, dashboards, experiment tracking tools)
• Excellent communication skills and ability to present analytical insights clearly

Preferred qualifications

• Contributions across multiple Kaggle tracks (Notebooks, Datasets, Discussions, Code)
• Experience in AI labs, fintech, product analytics, or ML-driven organizations
• Knowledge of LLMs, embeddings, and modern ML techniques for text, image, and multimodal data
• Experience with big data ecosystems (Spark, Ray, Snowflake, BigQuery, etc.)
• Familiarity with Bayesian methods or probabilistic programming frameworks
• Work on cutting-edge AI research workflows
• Collaborate with world-class data scientists and ML engineers
• Solve high-impact, real-world data science challenges
• Experiment with advanced modeling strategies and competition-grade validation techniques
• Flexible engagement options ideal for Kaggle-level problem solvers