Airswift is seeking an Business Data & AI Specialist (Engineering Analyst IV) to work on a 1-year contract basis with a major client in Calgary, Alberta.

The role requires strong technical execution skills with a focus on understanding the business context, delivering measurable outcomes, and ensuring solutions are usable, auditable, and aligned with how the business operates. This role leverages technology platforms to build and deliver business-owned solutions that address GTM-specific needs.

Key Responsibilities

Partner with business stakeholders across operations, engineering, reliability, records, and asset management to identify, scope, and prioritize data and AI opportunities. Translate business questions and operational pain points into well-defined analytical or modelling problems. Ensure solutions are grounded in business context, regulatory requirements, and operational realities. Perform data acquisition, cleansing, transformation, and validation across structured and unstructured datasets. Conduct exploratory analysis to surface trends, anomalies, risks, and improvement opportunities relevant to GTM operations. Design, build, test, and tune machine learning models using established techniques (e.g., classification, regression, clustering, natural language processing) to address specific business use cases. Build Generative AI and Agentic AI‑based solutions, including prompt engineering and workflow automation. Deliver reproducible analyses and clearly communicate findings, recommendations, and limitations to both technical and non-technical audiences. Create business-facing visualizations and dashboards that support day-to-day decision-making. Prepare and maintain documentation that supports knowledge transfer, auditability, and operational continuity. Apply appropriate evaluation methodologies and document assumptions, limitations, and model performance. Write clean, well-structured Python code that meets quality and security standards, working within shared repositories (Git). Support model deployment and operationalization, including basic MLOps practices such as monitoring inputs, outputs, and performance over time. Collaborate with D&SS, TIS, business partners, and domain experts to ensure solutions meet operational needs. Identify opportunities to enhance or extend existing business solutions within the assigned domain. Stay current on practical advances in data science, ML, and AI that are relevant to the business context.

Required Qualifications

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field. 6-8 years of combined experience applying data, analytics, and AI/ML to business or operational problems, with demonstrated ability to translate business needs into practical, data-driven solutions in the energy industry. Strong proficiency in Python and common data science libraries. Solid understanding of applied machine learning concepts and applied statistics. Demonstrated ability to communicate insights and recommendations to business stakeholders clearly and concisely. Experience working collaboratively across functions, not just within a technical team. Ability to work independently within a defined scope and effectively collaborate across teams.

Preferred Qualifications

Experience with Generative AI, large language models (LLMs), or agent‑based workflows or agent-based workflows in applied business settings. SQL proficiency and experience working with operational or analytical databases. Experience building business-facing dashboards or reports (e.g., Power BI). Familiarity with cloud-based platforms (Azure preferred; AWS or GCP acceptable). Experience working in engineering, operations, regulated, or energy sector environments. Exposure to documentation-heavy or audit-sensitive work contexts.

Level & Scope Expectations

Works independently on routine and moderately complex tasks within a defined business domain. Demonstrates depth in understanding the operational context behind the data. Produces solutions that are reliable, documented, supportable, and business-owned. Escalates complex, ambiguous, or cross-functional issues appropriately. Prioritizes consistent delivery and operational value over exploratory research or technology experimentation.