Translational Research in Oncology (TRIO) is a global academic clinical research organization dedicated to advancing translational cancer research in the clinical trial setting. Our passionate team is committed to providing cancer treatments of the future to the world of today. TRIO is looking for a Data Analysis Clerk to join our Monitoring Resource team. Reporting to the MR Administrator Manager, this position will be home-based in Edmonton. This position is a full-time, hourly position for the months of May – August 2026. Responsibilities: This position is responsible for performing any/all of the following tasks and responsibilities, as well as any other tasks deemed appropriate by the Supervisor: - In preparation for trial data analysis, produce trackers on outstanding data entry, open queries and outstanding SDV at the frequency required by the Study Lead MRc - Status update reports, as required, using a Clinical Trial Management System (CTMS) - Updating and configuring of large Excel files (tens of thousands of rows) extracted from various systems, in order for the team to perform data analytics - Work extensively with data in Excel using advanced Excel functions such as VLOOKUP, CONCATENATE, Pivot tables, and advanced formulas such as IF, IF AND, TODAY - Participate in the support of the MR team - Other duties as necessary Qualifications: - Related Degree in progress - Advanced knowledge in Excel and the ability to format large amounts of data. - 1-year work experience - Effective organizational skills, accuracy, and attention to detail - Ability to work independently, be self-motivated, and prioritize work effectively What We Can Offer You: - $18.00 per hour - 37.5 hours per week, Monday to Friday - Remote Work Arrangement - Temporary Summer Work Term We thank all candidates for their interest; only those selected for an interview will be contacted. Prior to applying please review TRIO's Applicant Information Notice To support efficiency, TRIO may use limited AI tools during the candidate review process. All hiring decisions are made by people, not AI. To ensure fairness and an accurate understanding of your skills, we ask that candidates refrain from using AI tools or AI‑generated responses during interviews or assessments. We want the opportunity to get to know you and your experience. Teamwork · Passion · Integrity · Innovation

• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. • Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). • Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. • Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. • Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). • Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. • Monitor model performance, conduct error analysis, and continuously optimize pipelines.

Key Responsibilities - Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. - Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). - Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. - Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. - Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). - Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. - Monitor model performance, conduct error analysis, and continuously optimize pipelines.   Required Skills - 5–8 years of experience in data science with deep hands‑on expertise in NLP and Generative AI. - Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK). - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

Key Responsibilities - Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. - Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). - Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. - Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. - Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). - Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. - Monitor model performance, conduct error analysis, and continuously optimize pipelines.   Required Skills - 5–8 years of experience in data science with deep hands‑on expertise in NLP and Generative AI. - Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK). - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.