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

• 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

• Python
• Pandas
• NumPy
• Polars
• scikit-learn
• SQL
• Distributed datasets
• Dashboards
• Experiment tracking tools
• Communication skills
• LLMs
• Embeddings
• Modern ML techniques for text, image, and multimodal data
• Spark
• Ray
• Snowflake
• BigQuery
• Bayesian methods
• Probabilistic programming frameworks

• 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