DevSecOps Product Developer

JANUS Research Group•Austin, TX

3d•Hybrid

About The Position

We are seeking a highly skilled Data Scientist with a strong background in software deployment, platform engineering, and path-to-production strategies for data science applications. In this role, you will bridge the gap between data science development and enterprise IT infrastructure, ensuring that custom application models written in Python and R are smoothly, securely, and reliably deployed, monitored, and maintained in production.

Requirements

Active Secret clearance
Strong background in software deployment
Strong background in platform engineering
Strong background in path-to-production strategies for data science applications
Experience with Python and R
Experience with CI/CD pipelines
Experience with containerization (e.g., Docker)
Experience with application monitoring and logging
Experience with cybersecurity policies and compliance
Experience with troubleshooting and incident response
Experience with data science platform tooling

Responsibilities

Architect, execute, and streamline path-to-production workflows for custom data science applications, interactive dashboards, and API models developed in Python and R.
Partner closely with DevSecOps team to design, build, and maintain robust CI/CD pipelines to automate testing, build, and deployment processes for analytics projects.
Implement and maintain robust application monitoring, logging, and metrics tracking to observe runtime health, system resource utilization, latency, uptime, and model drift in production environments.
Manage, build, and update custom container images and runtime environments to ensure reproducibility and consistency across development, staging, and production environments.
Partner closely with Cybersecurity and Governance teams to ensure all application deployments adhere to strict cybersecurity policies, vulnerability patching schedules, and enterprise compliance postures.
Act as the primary technical point of contact for diagnosing, debugging, and resolving deployment, environment, performance bottlenecks, and runtime errors for live data science applications.
Support and optimize modern data science platform tooling, enabling data science teams to seamlessly publish and monitor models, interactive dashboards, and reports.