At ProSource, we build and manage highly technical distributed teams for some of the most innovative companies in the world. We believe in humanizing the outsourcing industry by finding, attracting, and retaining top talent. Our dynamic workspaces promote creativity, collaboration, and well-being. We leverage smart technologies to ensure our clients and employees thrive in a collaborative, high-performing environment.

Role Overview:

We’re seeking a mid-career Python Developer (Production Pipeline) to help take our internally developed data science calculation pipeline and turn it into a production-ready, reliable, and maintainable system for customer-facing use.

This is a cross-functional role working closely with our Lead Data Scientist and CTO, bridging data science and engineering to ensure our calculators can run securely and consistently within a live customer portal environment. You’ll have meaningful input into system design and engineering standards, because our pipeline is still young and evolving rapidly.

Key Responsibilities:

Productionize our calculation pipeline by improving structure, maintainability, and reliability without compromising scientific integrity.
Design and implement comprehensive automated testing (unit, integration, regression) to support safe iteration and refactoring.
Strengthen pragmatic engineering standards for a fast-moving startup environment (e.g., CI discipline, code review habits, documentation, release hygiene).
Harden the pipeline against real-world usage: input validation, failure modes, edge cases, clear error handling and diagnostics.
Improve operational readiness for portal execution including logging, traceability, auditability of model decisions, and reproducibility of results.
Collaborate with the CTO on deployment/runtime considerations, including Kubernetes, Azure data access, and local Docker-based development/testing workflows.
Work closely with data science/modelling teammates to ensure changes align with scientific methodology, assumptions, and expected outputs.
Translate technical decisions and risks into plain English for mixed technical audiences, supporting alignment in a small, highly collaborative team.
Contribute to continuous improvement of processes, tooling, and system design as the product matures.

Qualifications:

3-5 years Python development experience, maintaining and improving production-grade codebases.
Strong testing experience (e.g., pytest), including designing test strategies that reduce regressions and improve reliability.
Solid software engineering fundamentals: modular design, clean code, dependency management, packaging, Git workflows.
Confident collaborating across functions (data science + engineering) with strong people ,skills and pragmatic communication.
Experience with Docker and containerized development; confidence working with cloud/deployment environments Kubernetes highly desirable).
Experience with data-intensive pipelines, including data validation, schema management, and messy real-world inputs.
Comfort working with varied datasets; geospatial and/or large dataset experience is a plus.
Interest in sustainability domains (emissions, carbon sequestration estimation, climate risk) is preferred.
Experience working closely with data science/modelling teams (shipping analytical code into production).
Familiarity with Azure data services/storage patterns.
Experience building reliability/observability patterns (structured logging, tracing, reproducible runs, audit trails).
Exposure to regulated or assurance-adjacent contexts (where outputs must be explainable and defensible).