Data Scientist

Evlo AI; Minneapolis (MN); On-site; USD 110,000 - 170,000; Full time

Job summary

Evlo AI seeks a data scientist to own the end-to-end data science lifecycle, turning large datasets into predictive insights and robust ML models that drive core business decisions. You will partner with data engineers, product managers, and software teams to build scalable analytics infrastructure and advanced statistical algorithms.

You will design and execute rigorous A/B tests, deploy production models, monitor performance, and communicate results to cross-functional stakeholders to ensure

Qualifications

• 3–6 years of experience in data science, quantitative analysis, or applied ML in production environments.
• Advanced proficiency in Python, SQL, and core data science libraries (Pandas, NumPy, scikit-learn, PyTorch).
• Experience designing and analyzing large-scale A/B experiments and statistical hypothesis testing.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker.
• BS or MS in Statistics, Applied Mathematics, Computer Science, or related quantitative field.

Responsibilities

• Develop and deploy advanced ML models and statistical algorithms for core business applications.
• Design and execute rigorous A/B tests to measure feature impact and algorithmic improvements.
• Build automated data cleaning, transformation, and feature engineering pipelines for large-scale data.
• Collaborate with data engineering to optimize data storage, retrieval, and schema design for modeling workflows.
• Present actionable insights and model performance metrics to cross-functional stakeholders.
• Monitor production models for performance drift and implement retraining strategies.

Skills

3-6 years experience

Python

SQL

Pandas/NumPy

scikit-learn

PyTorch

A/B testing

Statistics

LLM integrations

Education

BS/MS in Statistics/Applied Math/CS

Tools

AWS

GCP

Azure

Docker

PySpark

Job description

About The Role

The role owns the end-to-end data science lifecycle, turning complex datasets into predictive insights and robust machine learning models that drive core business decisions.

About The Role

The role owns the end-to-end data science lifecycle, turning complex datasets into predictive insights and robust machine learning models that drive core business decisions.

You will partner closely with data engineers, product managers, and software teams to build scalable analytics infrastructure and advanced statistical algorithms.

Key Responsibilities

• Develop and deploy advanced machine learning models and statistical algorithms using Python and SQL for core business applications


• Design and execute rigorous A/B tests to measure feature impact and validate algorithmic improvements


• Build automated data cleaning, transformation, and feature engineering pipelines operating on large-scale datasets


• Collaborate with data engineering teams to optimize data storage, retrieval, and schema design for modeling workflows


• Present actionable insights, technical findings, and model performance metrics to cross-functional stakeholders


• Monitor deployed production models for performance drift and implement retraining strategies

What We Are Looking For

• 3-6 years of experience in data science, quantitative analysis, or applied machine learning within production environments


• Advanced proficiency in Python, SQL, and core data science libraries such as Pandas, NumPy, scikit-learn, and PyTorch


• Demonstrated experience designing and analyzing large-scale A/B experiments and statistical hypothesis testing


• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker


• BS or MS in Statistics, Applied Mathematics, Computer Science, or a related quantitative field


• Bonus: Experience with LLM integrations, PySpark, or real-time streaming architectures