Data Scientist I, Production Analytics

Position Summary

As Data Scientist I, you will join the Data Science team to help run and support the analytics already in production, including client configurations, recurring reporting workflows, detection logic, and underlying code and pipelines. You will focus initially on recurring work and learning how the systems fit together: configuring clients, validating scheduled reports, and investigating issues with a senior data scientist. As you gain knowledge, you will take on more of this work independently and contribute directly to the production codebase. This is a hands-on technical role designed for growth, learning our claims data, statistical methodology, and a large production Python codebase.

Key Responsibilities

• Support production analytics and client reporting

• Configure new clients for analytical workflows and reports, and maintain configurations.

• Validate monthly and quarterly reporting cadences, confirming job execution and output accuracy.

• Investigate and resolve failures or unexpected results, or escalate with findings.

• Support client-specific requests for claims examples, analytical validation, and supporting data.

• Automate manual steps, add validation checks, and document configurations, issues, and resolutions.

• Learn and contribute to the production codebase

• Contribute to changes for security, compliance, and support (e.g., authentication updates, framework upgrades).

• Pair with senior data scientists and engineers on codebase changes.

• Read and understand existing Python code, tracing workflows and identifying pattern usage.

• Write and update unit tests, take changes through code review and CI, and validate in a development environment.

• Understand how changes behave in production, considering job longevity, data volume, and specific client paths.

• Collaborate with Data Engineering and Platform Engineering on data quality, pipeline, and infrastructure issues.

• Investigate and help improve existing analytical logic

• Monitor output quality of production statistical detectors and scoring logic.

• Investigate detectors flagged as noisy, low-value, or behaving unexpectedly.

• Handle well-scoped fixes independently (e.g., adjusting parameters, thresholds, configuration).

• Bring analysis and recommendations for changes to statistical methodology to a senior data scientist.

• Document changes, expected impact, and validation evidence.

• Flag detectors for rework, replacement, or retirement.

• Collaborate across teams

• Work with Data Science, Customer Success, Data Engineering, Platform Engineering, and Product.

• Translate client and Customer Success feedback into analytical or technical changes.

• Communicate issues clearly, including root cause, client impact, resolution, and prevention.

• Help build processes to scale analytics as the client base grows.

Qualifications

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

• 0–2 years of experience in data science, analytics, data engineering, or software engineering. Internships and academic projects count.

• Working proficiency in Python and SQL.

• Familiarity with git and the basics of code review and testing.

• Foundational understanding of statistics (distributions, variability, signal from noise).

• Strong debugging instincts.

• Strong attention to detail.

• Ability to manage several recurring deliverables on schedule.

• Clear written communication and good judgment on when to seek help.

• Genuine curiosity about complex healthcare data and a willingness to learn the payment integrity domain.

• Remote Work Requirements: Stable high-speed internet connection.

• Physical Requirements: Ability to work on a computer and standard office equipment.

Nice to Have

• Experience with healthcare claims data or healthcare analytics, and coding systems such as ICD-10, CPT, or HCPCS.
• Experience with pandas, NumPy, or comparable Python data libraries.
• Exposure to production data pipelines or scheduled workflows (e.g., Airflow).
• Experience with AWS or another major cloud platform; Spark or other distributed processing.
• Experience with SQL-based data warehouses and query performance tuning.
• Practical experience with statistical testing, regression, anomaly detection, or clustering.
• Awareness of security or compliance requirements in a regulated environment (e.g., HIPAA, FedRAMP, SOC 2).
• Familiarity with machine learning concepts and model evaluation.