Averna Technologies Inc.

Title: AI Data Analyst - temprop="jobLocation" itemscope="" itemtype="http://schema.org/Place">temprop="address" itemscope="" itemtype="http://schema.org/PostalAddress">Employees can work remotely - temprop="employmentType" style="color:#5e595e; font-size:14px">Full-time - Work Model: 100% Remote - City: Montreal - Department: Engineering & Cons Company Description In January 2026, Averna and Spherea joined forces to form the Spherea Group. A global leader in testing and quality solutions, Averna Powered by Spherea collaborates with product designers, developers, and manufacturers to help them improve product quality, accelerate time to market, and protect their brands, covering the entire product lifecycle with comprehensive solutions and expertise. Averna Powered by Spherea offers specialist expertise and innovative solutions in testing, vision inspection, precision assembly and automation, providing significant technical, financial and commercial advantages to customers in the aerospace, automotive, consumer electronics, defense, energy, industrial, medical device and life sciences, semiconductor, telecommunications and transportation sectors. Its solutions include prototyping and consulting, precision assembly and production, automated testing solutions, online testing systems, test system replication, and testing platforms and products. Averna Powered by Spherea's expertise covers vision systems, specialized battery testing, radio frequency and microwave technology, fiber optics, robotics and motion, instrumentation, control systems, and data management. Job Description AI Data Analyst -- Material Production, Quality and Reliability Role Summary This role sits at the intersection of data analytics, hardware production, quality and reliability engineering, and digital transformation . As an AI data analyst, you will generate value-added analytics from large-scale data related to production, testing, and deployments, while supporting the New Product Introduction (NPI) and product operations teams. You will transform complex production and quality concepts into data-driven metrics, intelligent dashboards, and AI-powered applications. You will also lead initiatives to modernize manual processes into automated, scalable, and analytics-driven systems—leveraging statistical analysis, cloud data platforms, and AI technologies. Main responsibilities Data analysis and engineering - Extract, transform and analyze large-scale production, testing, deployment and operations data using advanced SQL (DML). - Understanding and translating production, quality and reliability concepts into measurable, data-driven solutions and key performance indicators (KPIs). - Perform statistical analyses on production test data to ensure its integrity, reliability and readiness for automation and root cause analysis of deviations. - Provide continuous upstream feedback to improve data quality, coverage, and performance. AI, automation and intelligence - Identify and implement opportunities to automate manual processes using AI and advanced analytics. - Develop AI-powered recommendation systems for data normalization, anomaly detection, intelligent data mapping, and quality analysis. - Support the integration of AI and machine learning services (e.g., Vertex AI, LLM) into analytics workflows and intelligent applications. - Collaborating with engineers and product teams to integrate AI capabilities into NPI and production processes. Visualization, reports and executive analytics - Design, develop and maintain interactive dashboards and reports using tools such as Looker Studio, Tableau, Power BI or equivalents. - Visualize production performance, data quality indicators, operational metrics, staffing levels, and organizational expenditures. - Prepare summaries and presentations for management, simplifying complex technical data into clear and actionable analyses. - Providing management with real-time visibility to facilitate decision-making regarding the health of operations and production. Cross-functional collaboration and program support - Interact professionally with engineers, product managers, suppliers and subject matter experts in the areas of production, quality, IT and operations. - Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. - Supporting multiple initiatives in parallel by monitoring progress, identifying risks, and escalating delivery obstacles when necessary. - Facilitate change management through documentation, communication plans, and process training. Qualifications The ideal candidate in a nutshell: Required qualifications - Bachelor's degree in computer science, data science, data analytics, statistics, mathematics, engineering or related discipline. - Advanced proficiency in SQL and a thorough understanding of best practices in data modeling and normalization. - Proficiency in Python (or R) for data analysis, including experience with the PyData ecosystem (NumPy, Pandas, Matplotlib, scikit-learn). - Excellent skills in analysis, problem-solving, and statistical analysis. - Demonstrated experience in creating structured dashboards, reports, and datasets to support business and engineering decision-making. - Excellent oral and written communication skills in English. Desired qualifications (assets) - Over 5 years of experience with cloud data warehouses such as Google BigQuery or equivalent platforms. - Familiarity with Google Cloud data products (Vertex AI, Colab, Cloud API). - Over 5 years of experience in creating interactive dashboards with Looker Studio, Tableau, Power BI or similar tools. - Hands-on experience with AI/ML models, large language models (LLM) and AI process automation. - Experience in the field of electronic manufacturing processes, quality engineering, testing or reliability. - Knowledge of the concepts of data governance, data quality management or master data management (MDM). - Familiarity with operational topics such as workforce planning, resource allocation, or supplier/contractor monitoring (TVC). - Proficiency in Mandarin or French is an asset. Additional Information What we offer you - Learn and grow through several cutting-edge projects - Working remotely #LI-REMOTE - An extra day off for your birthday - Competitive total compensation and benefits package - Paid floating holidays between Christmas and New Year's Day - Significant discount on Montreal public transit passes - Be part of a company that places ESG criteria at the heart of its mission for people, planet and performance.

• Lead and coordinate technical projects from initiation through completion. • Manage project plans, schedules, risks, issues, and dependencies to ensure successful delivery. • Serve as the primary liaison between internal teams, customers, partners, and other stakeholders. • Facilitate onboarding, training, and adoption of technical processes and tools. • Provide guidance and support to technical and non-technical stakeholders throughout project execution. • Develop and maintain project documentation, process guides, and operational best practices. • Monitor project performance and identify opportunities for process improvement and increased efficiency. • Track project milestones, communicate status updates, and escalate issues when appropriate. • Support organizational change management activities, including communications, training, and documentation. • Collaborate with engineering, operations, and business teams to align project goals and priorities. • Contribute to business development activities by providing technical project insights and supporting customer discussions when required.

• Lead and coordinate technical projects from initiation through completion. • Manage project plans, schedules, risks, issues, and dependencies to ensure successful delivery. • Serve as the primary liaison between internal teams, customers, partners, and other stakeholders. • Facilitate onboarding, training, and adoption of technical processes and tools. • Provide guidance and support to technical and non-technical stakeholders throughout project execution. • Develop and maintain project documentation, process guides, and operational best practices. • Monitor project performance and identify opportunities for process improvement and increased efficiency. • Track project milestones, communicate status updates, and escalate issues when appropriate. • Support organizational change management activities, including communications, training, and documentation. • Collaborate with engineering, operations, and business teams to align project goals and priorities. • Contribute to business development activities by providing technical project insights and supporting customer discussions when required.

• Lead and coordinate technical projects from initiation through completion. • Manage project plans, schedules, risks, issues, and dependencies to ensure successful delivery. • Serve as the primary liaison between internal teams, customers, partners, and other stakeholders. • Facilitate onboarding, training, and adoption of technical processes and tools. • Provide guidance and support to technical and non-technical stakeholders throughout project execution. • Develop and maintain project documentation, process guides, and operational best practices. • Monitor project performance and identify opportunities for process improvement and increased efficiency. • Track project milestones, communicate status updates, and escalate issues when appropriate. • Support organizational change management activities, including communications, training, and documentation. • Collaborate with engineering, operations, and business teams to align project goals and priorities. • Contribute to business development activities by providing technical project insights and supporting customer discussions when required.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML) • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis • Continuously provide feedback upstream to improve data quality, coverage, and performance • Identify and implement opportunities to automate manual workflows using AI and advanced analytics • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed • Facilitate change management through documentation, communication plans, and process training.