Job Title: AI Productivity Analyst Location: Remote, Canada (Supporting EST hours) Duration: 6 months contract with (Possibility of extension) Pay range: $70/hr. to $75/hr. on T4 (all inclusive)

Interview rounds: 3 Rounds 1st, (HM round), 2nd Tech round (Coding in SQL), 3rd round with (Director of Analytics)

Note from manager: AI Productivity Analyst Cursor, Claude Code - how these products are used within Client’s Data Analyst with deep Analytics experience, minimum 8 years’ experience Focus heavily on AI productivity Matrix Exp in Saas company is required Someone already using these AI tools - Cursor, Claude Code Rich history in how matrix is performing Expert in SQL, Power-BI Product Analytics, Developer Productivity

Top must haves: AI Tools Experience: Cursor, Claude Code (hands-on usage) SQL (Expert level): Large-scale data querying & analysis Power BI / Dashboarding: Build impactful reports Python: Data analysis, modeling Product / Usage Analytics: Track adoption, behavior, patterns AI Productivity Metrics: Measure impact (time saved, quality, efficiency) Data Modeling: Semantic layers, clean datasets Statistical Analysis: Impact evaluation, forecasting SaaS / Tech Environment Experience

Position Overview: Client is accelerating how teams use AI native and AI augmented tools to improve productivity, product outcomes, and operational decision making across our platform organization. As adoption grows, we need a clearer, more rigorous understanding of what is working, where AI tooling is creating measurable value, and how those insights should inform future investment and product direction. The AI Productivity Analyst will help define, measure, and communicate the success of these AI Tooling initiatives. This role will partner closely with product, engineering, strategy, and finance stakeholders to build trusted metrics, bring disparate siloed sources of data together, uncover usage and adoption patterns, evaluate impact, and translate complex data into clear recommendations that help teams scale the right AI capabilities and workflows for teams.

Key Responsibilities: Partner with product managers, engineering leaders, and cross-functional stakeholders to define the business questions, success metrics, and analytical approach for AI tooling, platform productivity, and emerging technology initiatives. Use your analytical skills to derive actionable insights from large and complex datasets and communicate findings in a clear and concise manner to technical and non-technical audiences. Monitor KPIs and platform outcomes across platform and AI-enabled initiatives, providing regular insight into performance, trends, risks, and opportunities for leadership and product teams. Analyze large and complex datasets to understand how customers and internal teams use AI-enabled capabilities, identify friction points, and surface opportunities to improve product experiences and strategic investment decisions. Create clear and impactful data visualizations using tools such as Power BI, Looker, or similar, to communicate findings and recommendations to stakeholders across the organization. Apply statistical analysis, experimentation, forecasting, and modeling techniques to evaluate initiative impact, predict outcomes, and guide data-informed prioritization across the roadmap. Ensure data accuracy, consistency, and integrity by following best practices in data collection, cleaning, and analysis, allowing for high-quality insights. Identify and resolve data discrepancies as needed.

Required Qualifications: 8+ years of experience in large data analysis, business intelligence, or a related field, preferably within a technology or SaaS environment. Bachelor’s degree in a quantitative field such as Data Science, Statistics, Mathematics, or a related discipline. Experience using AI Tooling (Claude Code, Cursor etc.) to extend capabilities beyond traditional role boundaries Experience using AI Tooling (Claude Code, Cursor etc.) to accelerate and automate data analysis, and build lightweight systems and prototypes Strong skills in data querying and manipulation with SQL, and proficiency in programming languages like Python or R for data analysis and modeling. Experience with data warehouse ELT tools (Airflow, dbt, Snowflake). Experience designing and maintaining scalable analytics data models, semantic layers, and governed datasets that enable trusted self-service reporting and decision-making across the business. Experience with data visualization tools such as Looker, Power BI, or similar, with the ability to create clear, engaging, and impactful dashboards. Familiarity with AWS, Azure or similar cloud platforms. Proven analytical and problem-solving abilities, with the capability to work independently on complex datasets and derive actionable insights. Knowledge of statistical techniques and modeling methodologies to drive in-depth data insights and support predictive analytics. Strong ability to communicate complex data insights and recommendations to technical and non-technical stakeholders in a clear and accessible manner. Effective team player with a strong ability to work across functions, engage with diverse stakeholders, and provide data-driven support for strategic initiatives.

Preferred Qualifications: Prior experience working in a SaaS company or a platform engineering organization is preferred. Familiarity with Client’s product portfolio and its applications in various industries is an advantage. Master’s degree in a quantitative field such as Data Science, Statistics, Mathematics, or a related discipline.