Applied Scientist | Remote

Applied Scientists, Data Scientists & Machine Learning Researchers. Remote AI Project

We are seeking experienced Applied Scientists, Data Scientists, Machine Learning Researchers, and Quantitative Professionals to contribute to a remote AI project focused on advanced analytics, experimentation, statistical modeling, and machine learning.

You will analyze complex datasets, develop and evaluate machine learning models, design experiments, review AI-generated quantitative work, and create rigorous prompts, datasets, and reference solutions.

What You’ll Do

• Design and execute statistical analyses to identify meaningful patterns, relationships, and actionable insights from complex datasets.
• Develop, train, tune, and validate machine learning models for practical business or research problems.
• Perform large-scale data transformation, feature engineering, cleaning, and analysis using Python and Pandas.
• Design experimentation frameworks and evaluate experimental outcomes using appropriate statistical methods.
• Identify flawed methodology, incorrect assumptions, statistical errors, data leakage, and weak quantitative conclusions.
• Create expert-level prompts, datasets, analytical problems, and reference solutions.
• Evaluate model performance using appropriate metrics, validation strategies, and robustness checks.
• Document analytical methodology, results, limitations, and recommendations clearly.
• Deliver reproducible code, notebooks, and well-organized analytical reports.
• Review peer contributions and provide technically rigorous feedback.

Who Can Apply

Relevant backgrounds include Applied Scientists, Data Scientists, Senior Data Scientists, Research Data Scientists, and Analytics Scientists.

We also welcome Machine Learning Scientists, Machine Learning Researchers, Applied Machine Learning Scientists, and ML Engineers with strong statistical foundations.

Quantitative backgrounds may include Quantitative Analysts, Quantitative Researchers, Decision Scientists, and Computational Researchers.

Statistics backgrounds may include Statisticians, Applied Statisticians, Statistical Scientists, Biostatisticians, and Statistical Modeling Specialists.

Experimentation backgrounds may include Experimentation Scientists, Product Data Scientists, Causal Inference Specialists, A/B Testing Specialists, and Measurement Scientists.

Research backgrounds may include Research Scientists, Applied Researchers, Operations Research Scientists, Econometricians, and Computational Social Scientists.

Additional relevant backgrounds include Data Analysts with advanced modeling experience, Risk Modelers, Forecasting Scientists, Recommendation Systems Researchers, and Optimization Specialists.

Requirements

• At least one year of professional experience in applied science, data science, machine learning, quantitative research, statistics, or a closely related field.
• Experience at a leading technology company, financial institution, research organization, or similarly rigorous environment is highly relevant.
• Strong proficiency in Python, including Pandas and scikit-learn.
• Demonstrated experience in applied statistics, experimental design, and machine learning.
• Experience developing, tuning, and evaluating machine learning models in practical settings.
• Strong data modeling and data transformation skills.
• Ability to identify flawed methodology and statistical errors.
• Strong analytical and quantitative reasoning skills.
• Excellent written and verbal communication.
• Ability to produce clear technical documentation and reproducible analytical work.

Preferred Background

• Professional experience as an Applied Scientist, Data Scientist, Machine Learning Scientist, Quantitative Researcher, or similar role.
• Experience with experimentation, causal inference, forecasting, recommendation systems, optimization, or predictive modeling.
• Experience with Python, Pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, or TensorFlow.
• Experience working with large, complex, or messy datasets.
• Experience designing analytical frameworks, model evaluation systems, or data science benchmarks.
• Experience reviewing technical analyses, notebooks, research outputs, or model results for methodological quality.
• Experience collaborating with engineering, product, research, finance, or other cross-functional teams.
• Advanced academic background in Data Science, Statistics, Computer Science, Applied Mathematics, Economics, Operations Research, or a related quantitative discipline is valuable.

This opportunity is ideal for applied scientists who can take a complex data problem, choose an appropriate statistical or machine learning approach, build a rigorous and reproducible analysis, identify subtle methodological errors, and explain their conclusions clearly to both technical and non-technical audiences. We are a referral partner of the client.