Role overview
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.
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
- 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
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
Tags & focus areas
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