Data Scientist

Data Scientist at Keystone Recruitment

We are hiring on behalf of a leading AI research organization seeking an accomplished Data Scientist with demonstrated excellence in competitive machine learning environments (e.g., Kaggle or equivalent platforms). This role focuses on building high-performance models, designing rigorous experiments, and developing scalable data workflows that directly inform research and product innovation.

Role Overview

As a Senior Data Scientist, you will work at the intersection of experimentation, advanced modeling, and production analytics. You will analyze complex datasets, design robust validation frameworks, and build scalable machine learning pipelines across a range of modalities. This is an ideal opportunity for data scientists who thrive on competition-grade rigor and want to apply that mindset to real-world AI systems.

Key Responsibilities

• Analyze large and complex datasets to uncover patterns and modeling opportunities
• Develop predictive models across tabular, time-series, NLP, and multimodal datasets
• Design robust validation strategies and experimental frameworks
• Build reproducible data workflows and automated feature pipelines
• Conduct exploratory data analysis (EDA), hypothesis testing, and statistical investigations
• Collaborate with ML engineers to productionize models and ensure scalability
• Translate analytical findings into clear, structured recommendations
• Document methodologies and present insights through dashboards and reports

Required Qualifications

• Recognized achievement in competitive data science (e.g., Kaggle Grandmaster or equivalent high-level performance)
• 3–5+ years of experience in data science, machine learning, or applied analytics
• Strong proficiency in Python (Pandas, NumPy, scikit-learn, Polars, etc.)
• Experience building ML models end-to-end: feature engineering, training, evaluation, deployment
• Solid understanding of statistical methods, experiment design, and causal inference
• Experience working with SQL and modern data stacks
• Strong written and verbal communication skills

Preferred Qualifications

• Contributions across multiple competitive ML tracks (notebooks, datasets, discussions, etc.)
• Experience in AI research labs, fintech, or ML-first product environments
• Knowledge of LLMs, embeddings, and multimodal modeling techniques
• Experience with distributed systems (Spark, Ray) or cloud data warehouses (BigQuery, Snowflake)
• Familiarity with Bayesian modeling or probabilistic programming

What Success Looks Like

• High-performing, reproducible models with strong validation rigor
• Clear experimental design and defensible analytical conclusions
• Scalable pipelines ready for production integration
• Actionable insights that influence product and research direction

Engagement Details

• Remote, flexible work arrangement
• 30–40 hours per week (option for full-time engagement)
• Independent contractor engagement
• Compensation: $56–$77 per hour
• Project duration may vary based on scope and performance
• Weekly payments via secure payment platform

Key skills/competency

• Machine Learning
• Data Modeling
• Experiment Design
• Python Proficiency
• SQL
• Feature Engineering
• Statistical Analysis
• NLP (Natural Language Processing)
• Multimodal Data
• Predictive Analytics