Co-Op Student - Data Engineer, AI Readiness (Medical-Legal Data)

■ CO-OP STUDENT — DATA ENGINEER, AI READINESS (MEDICAL-LEGAL DATA)

(Fully Remote - Ontario or Quebec)

CONTRIBUTING TO THE CMPA

Roughly 80% of medical-adjacent data is unstructured or goes unused after it's created — and this, not model capability, is the real bottleneck for scaled AI adoption. A data engineer focused on AI readiness is responsible for turning raw, largely unstructured medical-legal records into governed, standardized, machine-consumable data assets — a foundational step that must precede any scaled AI roadmap at an organization like CMPA.

AI-readiness is a formally defined, auditable property, not a byproduct of general data science work. It requires dedicated engineering effort that most data scientists aren't resourced to prioritize. Deploying AI directly on ungoverned, untouched data creates brittle systems and real audit and compliance risk — a particularly serious exposure for an organization producing legally and clinically consequential outputs such as medical coding, the Physician Advisor support tool, Member Risk Profile, and self-learning materials. Done well, this role acts as a multiplier across every planned AI initiative, rather than a one-off data cleanup cost. Delaying this hire risks CMPA falling behind organizations that already treat their proprietary data as a governed, strategic asset.

You won't be doing one-off data cleanup — you'll be building the governed data foundation that every downstream AI initiative at CMPA depends on, from medical coding to the Physician Advisor support tool to Member Risk Profile. It's a rare opportunity to see how disciplined data engineering directly determines AI system reliability and audit-readiness in a regulated, high-stakes domain.

POSITION OVERVIEW

CMPA's AI & Advanced Analytics team builds AI solutions that process sensitive medical-legal content — physician-advisor call transcripts, Notes-to-File (NTFs), case documentation, and policy records. We are looking for a co-op student to focus on transforming raw medical-legal data into AI-ready datasets: cleaned, standardized, labeled, and governed so they can reliably train, fine-tune, and evaluate AI models. This role works entirely with internally hosted data and infrastructure (on-prem GPU servers) to maintain strict privacy and security for PHI/PII, and supports real production pipelines already in active development.

POSITION ACTIVITIES

• Clean, normalize, and structure raw text from call transcripts, NTFs, and case files into standardized, machine-consumable formats.
• Help define and apply AI-readiness criteria for medical-legal datasets — completeness, consistency, labeling accuracy, lineage, and auditability — so data quality becomes measurable rather than assumed.
• Support document classification and medical-legal coding workflows by preparing labeled datasets and validating tagging accuracy against existing coding schemes.
• Help build and refine hierarchical tagging/taxonomy structures for case documents and policy content, supporting case management and member-matching use cases.
• Assist with quality control on transcription and translation outputs (errors, formatting inconsistencies, PHI leakage risks) to improve downstream summarization accuracy.
• Develop scripts and light pipelines (Python) for ingesting, de-identifying, chunking, and formatting documents at scale, with traceability suitable for audit review.
• Create and maintain evaluation/test sets (e.g., scripted advisor-member conversation samples) to benchmark transcription, summarization, and classification performance.
• Document data preparation standards, labeling guidelines, and known data quality issues so the process is repeatable, governed, and auditable across teams.
• Work closely with the AI program lead to align data preparation work with governance, privacy, and security requirements for regulated healthcare data.

EDUCATION AND EXPERIENCE

• Currently enrolled in a co-op program in Computer Science, Data Science, Health Informatics, or a related technical discipline.
• Solid programming fundamentals in Python, including experience with text processing/NLP libraries (e.g., pandas, regex, spaCy, or similar).
• Strong attention to detail and comfort working with sensitive, high-stakes content requiring careful handling.
• Basic understanding of NLP concepts (tokenization, named entity recognition, classification) through coursework or projects.
• Ability to work methodically with ambiguous, messy real-world data and document your process clearly enough for others (and auditors) to follow.

SKILLS AND ABILITIES

• Prior exposure to healthcare, legal, or insurance domain data.
• Familiarity with de-identification/anonymization techniques for PHI/PII.
• Exposure to data governance, data quality frameworks, or audit/compliance concepts.
• Experience with annotation/labeling tools or building custom labeling workflows.
• Exposure to speech-to-text or document OCR pipelines.

POSTING DETAILS

• Job type: This is a Temporary Full-Time position from January 2027 to April 2027 with possibility for extension
• This position is for an existing vacancy
• Salary range: Between $22.00-$31.00/hour based on level of education and previous relevant work experience.
• Location: Fully Remote Job, working from a home-based office anywhere in the provinces of Ontario and Québec.
• Students are defined as individuals who are enrolled in full-time studies at a post-secondary institution during the year that they are employed. To be considered for a position, candidates must be enrolled in an academic program from January 2027 to April 2027
• Skills assessment: Selected candidates may be required to complete a skill assessment
• Application deadline: October 9, 2026 at 4:00PM EST

The CMPA is an equal opportunity employer and is committed to being responsive to those living with disabilities and strives to prevent and remove barriers to accessibility. The CMPA will provide support and accommodation in its recruitment processes to applicants living with disabilities. If you are invited to participate in an interview and/or skills assessment and have accommodation needs, please let us know.

Equity, diversity, and inclusion (EDI) is a key priority, and we actively strive to build a culture of inclusion where employees can be their authentic selves and are valued for their diverse experiences and perspectives.

We welcome and encourage candidates from diverse backgrounds and a variety of lived experiences to apply.

The CMPA offices, located in Ottawa, are located on the unceded, unsurrendered Territory of the Anishinaabe Algonquin Nation, whose presence here reaches back to time immemorial. We honour and pay our respect to these lands, and to all First Nations, Inuit and Métis Peoples throughout Turtle Island.