Data Scientist

Zywave; United States; On-site; USD 80,000 - 120,000; Full time

Job summary

A technology company is looking for a Data Scientist to work in their AI Engineering and Data Science team. The role involves implementing evaluation pipelines, analyzing data, and maintaining machine learning models. Candidates should have a strong foundation in statistics, machine learning skills, and proficiency in Python and SQL. The position requires self-motivation and effective communication skills to report findings to stakeholders. This opportunity allows for impactful work in AI and machine learning projects.

Qualifications

• Strong foundation in statistics and experimental design.
• Hands-on experience with machine learning fundamentals.
• Proficiency in Python and the data science stack.
• Strong SQL skills with relational databases.

Responsibilities

• Implement evaluation pipelines for AI models.
• Execute benchmark tests and collect performance metrics.
• Build and maintain classical machine learning models.
• Develop and maintain dashboards tracking model performance.

Skills

Statistical analysis

Machine learning fundamentals

Python

SQL

Data pipelines

Analytical skills

Communication skills

Tools

pandas

scikit-learn

NumPy

matplotlib/seaborn

Git

Job description

At Zywave, we believe in building AI systems that are reliable, measurable, and continuously

improving. The Data Scientist will work within our AI Engineering and Data Science team to

execute and operationalize our evaluation framework for ML/AI models. This role is hands‑on

and execution‑focused, requiring strong technical skills in statistical analysis, and production ML

workflows. You'll implement evaluation pipelines, run experiments, and conduct analyses that

ensure our AI systems meet quality standards and drive measurable business impact.

What you will do:

Evaluation Framework Execution

• Implement and operationalize evaluation pipelines for agentic AI models based on established frameworks and methodologies.
• Execute benchmark tests, collect performance metrics, and generate evaluation reports.
• Maintain and improve automated testing suites that assess model performance across
• Monitor evaluation results and flag performance issues or anomalies for investigation.
• Execute A/B tests and experiments according to designed protocols, ensuring proper implementation and data collection.
• Conduct statistical analysis of experiment results, including hypothesis testing, confidence intervals, and effect size calculations.
• Create clear, actionable reports that communicate experiment findings to stakeholders.
• Support the design of new experiments by providing data-driven insights and feasibility

Traditional ML & Statistical Analysis

• Build and maintain classical machine learning models to support business analytics and decision‑making.
• Perform statistical analyses, including regression analysis, time series forecasting, and cohort analysis.
• Develop predictive models for business metrics, user behavior, and model performance trends.
• Build and maintain data pipelines that support evaluation workflows, analytics, and reporting.
• Ensure data quality through validation checks, monitoring, and documentation.
• Work with data engineering teams to optimize data collection and storage for evaluation needs.
• Create and maintain SQL queries, transformations, and data models.

Monitoring & Reporting

• Develop and maintain dashboards that track model performance, experiment results, and key metrics.
• Create automated reporting that provides visibility into AI system health and business impact.
• Support incident response by analyzing data to identify root causes of performance issues.
• Document processes, methodologies, and findings to build organizational knowledge.

What you should bring:

• Strong foundation in statistics and experimental design—you understand A/B testing, hypothesis testing, and can properly analyze experiment results.
• Hands‑on experience with machine learning fundamentals, including model training, evaluation, and feature engineering.
• Proficiency in Python and the data science stack (pandas, scikit‑learn, NumPy, matplotlib/seaborn).
• Strong SQL skills and experience working with relational databases and data warehouses.
• Experience building data pipelines or working with workflow orchestration tools.
• Familiarity with version control (Git) and collaborative software development practices.
• Strong analytical and problem‑solving skills with attention to detail.
• Good communication skills—you can explain technical concepts clearly and create
• Self‑motivated with the ability to manage multiple tasks and priorities effectively.
• (Preferred) Exposure to generative AI, LLM evaluation, or working with AI/ML products