Data Scientist

Evlo AI; Seattle (WA); On-site; USD 120,000 - 160,000; Full time

Job summary

Evlo AI is seeking a data scientist to own the complete data science lifecycle, turning high-volume datasets into predictive models that drive product growth.

The team collaborates with product managers, data engineers, and software developers to build scalable ML solutions for high-throughput production environments.

Qualifications

• 3–6 years of experience in data science, applied statistics, or quantitative modeling within a technology or product-driven organization.
• Advanced proficiency in Python and SQL, with strong data manipulation libraries.
• Experience deploying models into production using cloud platforms (AWS/GCP/Azure).
• Deep understanding of experimental design and advanced statistical techniques.
• Master’s or Ph.D. in a quantitative field.
• Bonus: experience with large language models or deep learning frameworks.

Responsibilities

• Develop and deploy ML models and statistical algorithms to solve high-impact challenges.
• Design robust feature stores and data pipelines with Python, SQL, and Spark for data integrity.
• Execute rigorous A/B testing and analysis with clear KPIs and causal inference.
• Collaborate with engineering to optimize model inference latency and throughput in cloud environments.
• Translate business objectives into analytical frameworks and deliverables.
• Mentor junior data scientists and advocate for rigorous statistical methodologies.

Skills

Python

SQL

Pandas

NumPy

scikit-learn

ML in production

A/B testing

Cloud (AWS/GCP/Azure)

Statistics

Hypothesis testing

Education

Master's or Ph.D. in Statistics/CS/Math/Economics

Tools

AWS

GCP

Azure

Spark

Job description

About The Role

The role owns the entire data science lifecycle, turning complex, high-volume datasets into predictive models and actionable decision-making frameworks that drive core product growth.

About The Role

The role owns the entire data science lifecycle, turning complex, high-volume datasets into predictive models and actionable decision-making frameworks that drive core product growth.

The team collaborates closely with product managers, data engineers, and software developers to build scalable ML solutions that operate seamlessly in high-throughput production environments.

Key Responsibilities

• Develop and deploy machine learning models and statistical algorithms to solve high-impact business and product challenges
• Design robust feature stores and data pipelines using Python, SQL, and Spark to ensure high data integrity across experimentation and production
• Execute rigorous A/B testing and experimentation analysis, establishing clear KPIs and causal inference frameworks for new product features
• Collaborate with engineering teams to optimize model inference latency, throughput, and resource utilization in cloud environments
• Translate ambiguous business objectives into well-defined analytical frameworks, predictive models, and technical deliverables
• Mentor junior data scientists, participate in peer code reviews, and advocate for rigorous statistical methodologies across the organization

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or quantitative modeling within a technology or product-driven organization
• Advanced proficiency in Python and SQL, with strong expertise in data manipulation libraries like pandas, NumPy, and scikit-learn
• Demonstrated experience deploying statistical and machine learning models into production systems using AWS, GCP, or Azure
• Deep understanding of experimental design, hypothesis testing, probability, and advanced statistical modeling techniques
• Master’s or Ph.D. degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field
• Bonus: Experience with large language models, deep learning frameworks (PyTorch/TensorFlow), or working in a hybrid Seattle-based engineering setup