Data Scientist I – Production Analytics

PLEASE NOTE BEFORE APPLYING:

CODOXO IS NOT ABLE TO OFFER SPONSORSHIP OR ACCOMMODATE ANY CANDIDATES THAT ARE CURRENTLY BEING SPONSORED NOW OR IN THE FUTURE

Of the $3.8T we spend on healthcare in the United States annually, about a third of it is estimated to be lost due to waste, fraud and abuse. Codoxo is the premier provider of artificial intelligence-driven solutions and services that help healthcare companies and agencies proactively detect and reduce risks from fraud, waste, and abuse and ensure payment integrity. Codoxo helps clients manage costs across network management, clinical care, provider coding and billing, payment integrity, and special investigation units. Our software-as-a service applications are built on our proven Forensic AI Engine, which uses patented AI-based technology to identify problems and suspicious behavior far faster and earlier than traditional techniques.

We are venture backed by some of the top investors in the country, with strong financials, and remain one of the fastest growing healthcare AI companies in the industry.

Position Summary:

Our Data Science team builds and runs the statistical detection, scoring, and reporting engines behind Codoxo’s products. Everything that team has shipped is now live across a growing client base, and supporting it well has become a substantial body of work in its own right.

That is where you will start. As Data Scientist I, you will join the Data Science team to help run and support the analytics we already have in production — the client configurations, the recurring reporting workflows, the detection logic, and the code and pipelines underneath them. Early on you will focus on the recurring work and on learning how our systems fit together: configuring clients onto existing products, validating scheduled reports, and investigating issues with a senior data scientist alongside you. As you build that knowledge, you will take on more of this work independently and contribute directly to the production codebase.

This is a hands-on technical role and a deliberate entry point into our platform. You will learn our claims data, our statistical methodology, and a large production Python codebase from people who built it, with support and code review throughout. It is a role designed to grow: the more of our production analytics you can carry, the more time our senior data scientists have for open research questions — reducing false positives, learning patterns across our client base, and catching new forms of fraud, waste, and abuse earlier — and the sooner you will be working on those questions yourself.

Key Responsibilities:

Support production analytics and client reporting

• Configure new clients to run the analytical workflows and reports included in their purchased solutions, and help maintain those configurations as client needs change.

• Validate the monthly and quarterly reporting cadences: confirm jobs execute on schedule, and validate output for accuracy and completeness before delivery.

• Investigate failures and unexpected results, and resolve them or escalate with a clear picture of what you found.

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

• Help turn recurring problems into durable fixes: automate manual steps, add validation checks that catch issues earlier, and document configurations, known issues, and resolutions.

Learn and contribute to the production codebase

• Contribute to the changes our production analytics code needs to stay secure, compliant, and supported — for example, updating how our jobs authenticate to our databases in response to a security requirement, or upgrading a framework version across the pipelines that depend on it. You will pair with senior data scientists and engineers on this work at first, and take on more of it as you go.

• Get comfortable in a large existing Python codebase: read code you did not write, trace how a workflow actually runs, and find every place a pattern is used rather than only the obvious ones.

• Write and update unit tests, take your changes through code review and CI, and validate them in a development environment before they reach production.

• Build an understanding of how a change behaves in production — not only whether it works, but whether it holds up inside a long-running job, at a large client’s data volume, or on a path that only one client exercises.

• Work with Data Engineering and Platform Engineering on data quality, pipeline, and infrastructure issues that cross team boundaries.

Investigate and help improve existing analytical logic

• Keep an eye on the output quality of our production statistical detectors and scoring logic.

• Investigate detectors flagged as noisy, low-value, or behaving unexpectedly: analyze the underlying claims and flagged entities to determine whether the cause is data quality, client configuration, detector parameters, or the statistical logic itself.

• Handle well-scoped fixes yourself once you know the area — adjusting parameters, thresholds, or configuration, then validating that the output genuinely improves.

• For changes that reach further into statistical methodology, bring your analysis and a recommendation to a senior data scientist and work with them to design and validate the change.

• Document what changed, the expected impact, and the validation evidence.

• Flag detectors that may need to be reworked, replaced, or retired.

Collaborate across teams

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

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

• Communicate issues clearly and completely: root cause, client impact, resolution, and what is needed to prevent recurrence.

• Help build the processes that let our analytics scale as the client base grows.

How You’ll Ramp Up

You are not expected to arrive knowing our systems, our data, or healthcare payment integrity. We expect to teach you.

• First weeks: shadow recurring reporting cycles, learn the products and the claims data, and take on your first client configurations and validation work with close support.

• First few months: run the recurring reporting cadences independently, investigate issues on your own, and make your first reviewed changes to the codebase and to detector configuration.

• Toward the end of your first year: own a meaningful share of our production analytics end to end, lead maintenance and compliance changes through review, and begin contributing to new analytical work.

Growth Path

As your depth in our data, code, and payment integrity methodology grows, the scope of your work grows with it — designing and evaluating new statistical detectors, contributing to new product capabilities, improving peer grouping and risk methodologies, and partnering with senior Data Scientists on open research problems.

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. Strong internships and academic projects count.

• Working proficiency in Python and SQL, and the confidence to read code you did not write.

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

• Foundational statistics: distributions, variability, and how to tell a real signal from noise.

• Strong debugging instincts — given an unexpected number, you are willing to work backward through data, code, configuration, and workflow to find where it went wrong.

• Strong attention to detail. Much of this work reaches clients, and quietly wrong output is worse than a failed job.

• Ability to keep several recurring deliverables on schedule.

• Clear written communication, and good judgment about when to keep going and when to ask for help.

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

• Remote Work Requirements: To ensure reliable performance on the company-issued CODOXO laptop, employees working remotely must have a stable high-speed internet connection. Internet performance should be measured using a speed test conducted directly on the CODOXO laptop, not based solely on the internet plan purchased from the provider.

• Physical Requirements: Work is performed in an office environment (either in our office or work-from home) and requires the ability to work on a computer, operate standard office equipment, and work at a desk.

Nice to Have

• Experience with healthcare claims data or healthcare analytics, and coding systems such as ICD-10, CPT, or HCPCS.

• pandas, NumPy, or comparable Python data libraries.

• Exposure to production data pipelines or scheduled workflows (Airflow or similar).

• AWS or another major cloud platform; Spark or other distributed processing.

• SQL-based data warehouses and query performance tuning.

• Statistical testing, regression, anomaly detection, or clustering in practice.

• Awareness of security or compliance requirements in a regulated environment (HIPAA, FedRAMP, SOC 2).

• Familiarity with machine learning concepts and model evaluation.

Accessibility Notice: If you need reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to careers@codoxo.com with the subject "Accommodation". Reasonable accommodation requests will be considered on a case-by-case basis.

Benefits for You

• Health, Dental, and Vision insurance with 100% employee premium coverage (Starts Day 1)

• Unlimited PTO

• Annual Professional Development stipend

• Annual home office stipend

• 401K Match (after 90 days)

We are an Equal Opportunity Employer:

Codoxo provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment.