Data Scientist - Kaggle Grandmaster

Engagement Type: Independent Contractor Work Mode: Fully Remote Hours: 30–40 hours/week or Full-Time (Flexible) About the Role We are partnering with a leading AI research lab to hire 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 collaborate closely with researchers and engineers to design rigorous experiments, build advanced statistical and machine learning models, and develop data-driven frameworks that support product and research decisions. Key Responsibilities Analyze large, complex datasets to uncover patterns and generate actionable insights Build predictive models and ML pipelines across: Tabular data Time-series data NLP Multimodal datasets Design and implement validation strategies, experimental 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 Translate analytical results into clear recommendations for engineering, product, and leadership teams Collaborate with ML engineers to productionize models and ensure reliable data workflows at scale Present findings via dashboards, structured reports, and documentation Required 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 Nice to Have 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 Why Join 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