Engagement Type: Independent Contractor
Work Mode: Fully Remote
Hours: 30–40 hours/week or Full-Time (Flexible)
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.
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
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
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
