Data Scientist

Evlo AI; Raleigh (NC); On-site; USD 120,000 - 160,000; Full time

Job summary

Evlo AI is seeking a data scientist to own end‑to‑end data science lifecycle, turning complex datasets into scalable predictive models and actionable insights. You will work with data engineers and product stakeholders to deliver measurable impact with reliable production models.

The role requires 3–6 years in data science, strong Python/SQL skills, and experience deploying models in cloud environments. A quantitative degree is expected; bonus for LLM integrations and MLflow experience.

Qualifications

• 3–6 years of data science or quantitative ML experience.
• Strong Python/SQL with ML libraries.
• Production model deployment in cloud environments.
• Solid stats, experimental design, hypothesis testing.
• Bachelor’s or Master’s in a quantitative field.
• Bonus: LLM integrations, MLflow, or peer-reviewed research publications.

Responsibilities

• Develop and deploy ML models and analytics pipelines.
• Perform exploratory data analysis and feature engineering using Python, SQL, and distributed computing like PySpark.
• Collaborate with data engineers to prepare data for training and inference.
• Evaluate models with metrics, run A/B tests, and improve accuracy.
• Communicate findings clearly to cross‑functional stakeholders and leadership.
• Contribute to internal tooling and code quality via peer reviews and documentation.

Skills

Python

SQL

Statistical modeling

A/B testing

Communication

Collaboration

Education

Bachelor’s or Master’s in Statistics/Computer Science/Mathematics/Economics

Tools

scikit-learn

pandas

NumPy

PyTorch/TensorFlow

MLflow

AWS/GCP/Azure

Job description

About The Role

The role owns the end-to-end data science lifecycle, turning complex datasets and business challenges into scalable predictive models and actionable insights.

The team collaborates closely with data engineers and product stakeholders to ensure models deliver measurable impact while maintaining high standards of accuracy and reliability in production.

Key Responsibilities

• Develop and deploy machine learning models, statistical frameworks, and advanced analytics pipelines to solve core business challenges


• Perform exploratory data analysis and feature engineering using Python, SQL, and distributed computing frameworks like PySpark


• Collaborate with data engineering teams to structure, clean, and optimize data assets for training and inference environments


• Evaluate model performance rigorously, tracking metrics, conducting A/B tests, and iterating to improve predictive accuracy


• Communicate complex technical findings and data-driven insights clearly to cross-functional stakeholders and leadership


• Contribute to internal data science tooling, best practices, and code quality standards through peer reviews and documentation

What We Are Looking For

• 3–6 years of professional experience in data science, applied statistics, or quantitative machine learning


• Advanced proficiency in Python and SQL, with strong hands-on experience using libraries such as scikit-learn, pandas, NumPy, and PyTorch or TensorFlow


• Demonstrated experience deploying and monitoring machine learning models in production cloud environments such as AWS, GCP, or Azure


• Solid foundation in statistical modeling, hypothesis testing, experimental design, and machine learning fundamentals


• Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field


• Bonus: Experience with LLM integrations, MLOps tooling like MLflow, or publishing research in peer-reviewed venues