Full Time / Internship
Early Career
Location: Edmonton, AB, Canada
Genesis Data Solutions is a tech-enabled materials corrosion and integrity management consultancy leveraging AI-ACRIM+ (AI for Advanced Corrosion Risk and Integrity Management and Predictive Analytics) to enhance corrosion management across oil & gas and wastewater systems.
With deep expertise in AI, data analytics, and corrosion engineering, we work with operators to prevent failures, optimize performance, and improve system reliability. By combining advanced technology with engineering insight, we deliver precise, efficient, and innovative corrosion solutions that reduce risk and operational cost.
At Genesis, we are transforming how corrosion risk and integrity are managed using AI-driven analytics and engineering intelligence.
As an AI/ML Developer, you will work on real-world industrial challenges, developing models and systems that power AI-ACRIM+. You will collaborate with corrosion engineers and software teams to build predictive solutions that directly impact asset performance and reliability.
You will design and develop advanced machine learning models and data systems that enable real-time corrosion risk assessment and integrity decision-making. Your work will directly contribute to building intelligent, scalable solutions for complex industrial systems.
• Design and implement data analysis pipelines to extract insights from diverse datasets
• Develop predictive models using machine learning techniques for corrosion and integrity applications
• Apply statistical methods to uncover patterns and support decision-making
• Integrate and transform data from multiple sources into unified analytical frameworks
• Build scalable backend systems and data infrastructure
• Develop and maintain ETL/ELT processes using tools such as Python, Spark, SQL, and Power BI
• Contribute to frontend interfaces for user-friendly visualization and interaction
• Collaborate with engineering teams to interpret results and improve models
• Ensure high standards of data integrity, performance, and system reliability
As part of Genesis, your work will directly support AI-ACRIM+, enabling organizations to move from reactive corrosion management to predictive, data-driven integrity strategies.
You will play a key role in building systems that improve safety, reduce failures, and enhance operational efficiency across critical infrastructure.
• Bachelor’s degree or higher in Computer Science, Statistics, or related field (or currently pursuing)
• Strong understanding of machine learning and statistical analysis
• Experience working with large and complex datasets
• Familiarity with data-driven problem solving in engineering or industrial contexts
• Experience in reporting, analysis, or proposal development
Technical Skills:
• Proficiency in Python, R, or similar programming languages
• Experience with data processing frameworks (e.g., Spark, SQL)
• Knowledge of ETL/ELT processes and data pipelines
• Familiarity with backend or full-stack development is an asset
• Experience with data visualization tools (e.g., Power BI) is preferred
• Master’s or Ph.D. in a relevant field
• Knowledge of software and data engineering principles
• Experience building user-focused data or software products
• Competitive compensation based on experience
• Potential share options
• Flexible work arrangements (remote / hybrid)
• Opportunities for professional growth and development
• Collaborative, multidisciplinary work environment
Equal Opportunity Employer:
Genesis Data Solutions is committed to fostering an inclusive and diverse workplace. We provide equal opportunities to all applicants regardless of background and support accommodations where required.
If you are passionate about AI, data, and solving real-world corrosion challenges, we encourage you to apply.
📩 Send your resume and cover letter to:
Tesfa Haile — tesfa.haile@genesis-datasolutions.com
Only shortlisted candidates will be contacted.
⚠️ Note: This position is subject to budget approval. Hiring will proceed once approvals are confirmed.
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