Senior Business Analyst

Eclipse Automation•Cambridge, ON

6d•CA$90,000 - CA$110,000•Onsite

About The Position

The AI Business Analyst will serve as the strategic bridge between business stakeholders and global technology teams, translating real-world business processes into AI-native solutions that drive measurable impact. This role blends deep curiosity about how work gets done with a strong focus on rapid, data-driven prototyping and deployment. Eclipse Automation is shaping the future of custom automation through a multi-year digital transformation initiative leveraging OpenUSD, NVIDIA Omniverse, and enterprise AI. The ideal candidate has a keen understanding of business operations and analytics to support data-driven decision making — someone who instinctively reaches for AI tools first, identifies inefficiencies, understands data security requirements.

Requirements

Successfully completed either a University Degree or College Diploma in Business Analyst, AI for Business Analysts, or related course
Minimum 5 years' experience in a business analyst capacity, with demonstrated proficiency leveraging AI tools for improved efficiency and workflow automation
Builder mindset required: Candidates should be prepared to demonstrate tools, applications, or automated workflows they have personally built using AI — whether in a professional or personal context. A successful candidate will show a portfolio of real solutions they've shipped, not just familiarity with concepts
Proficiency in data manipulation and programming languages for advanced data analysis and machine learning workflows
Experience leveraging machine learning libraries and cloud-based AI services for predictive and prescriptive modeling
Experience working with SQL, VB6, C#, .NET, Python, Azure, and Power BI
Comfortable across domains — operations, finance, product, support — and skilled at tailoring solutions to local constraints and scale requirements
Demonstrates strong verbal and written communication skills
Promotes positive relationships between departments and with partners, customers, vendors, and distributors
Recommends and administers policies and procedures that enhance operations and support continuous improvement
Ability to work in a team environment and actively participate in group meetings
Maintains good corporate citizenship in support of Eclipse's corporate vision
Ensures all business activities are performed with the highest ethical standards and in compliance with the Eclipse Automation Inc. Code of Conduct
Fosters a culture reflective of the core values of the business
Adheres to the Eclipse Project Operating Policy

Nice To Haves

Experience in the automation or manufacturing industry is an asset

Responsibilities

Discover and map: Step into any department to observe day-to-day operations, interview stakeholders, and map current-state processes with an emphasis on where decisions are made, handoffs occur, and repetitive work accumulates.
Identify high-value opportunities: Analyze process data and metrics to pinpoint inefficiencies, cost drivers, compliance risks, and areas where AI and automation can deliver the greatest return.
Rapid prototyping: Move beyond static artifacts—quickly prototype AI-driven solutions using off-the-shelf models, low-code tools, and in-house platforms. Build working demos in hours to validate concepts with users.
Define pragmatic requirements: Translate findings into clear, prioritized functional requirements, acceptance criteria, and success metrics that product and engineering teams can execute against.
Design human-centered solutions: Ensure AI enhances human work—streamline decision support, automate repetitive tasks, and surface insights so people can work faster and make better choices.
Collaborate across functions: Work closely with data team, UX designers, and operations leaders to iterate solutions, monitor outcomes, and scale proven pilots enterprise-wide
Measure and optimize: Establish baselines, track KPIs, and run experiments to quantify efficiency gains, cost savings, and user adoption; refine models and processes based on results
Governance and ethics: Embed responsible AI practices—data privacy, bias mitigation, explainability, and auditability—into solution designs and deployment plans.
Risk mitigation: Identify technical risks and their impact on the project and mitigate their effects through proactive planning and contingency design.
Change management: Lead change management initiatives to ensure adoption of new processes, tools, and standards; design communication plans, training materials, and feedback loops to sustain behavioral and operational change.