• Establish and maintain metadata governance frameworks, ensuring compliance with DoD data management policies and interoperability with Data Catalog. • Develop automated metadata tagging processes to improve data discoverability, classification, and lineage tracking within the Common Intelligence Picture (CIP) platform. • Implement and manage data governance workflows, ensuring integration with OpenLineage, Unity Catalog, and GIS metadata standards. • Submit the Metadata & Data Governance Compliance Report, detailing lineage tracking, classification policies, and security enhancements.

• Collect, integrate, clean, validate, and analyze workforce and training data from multiple sources. • Design and implement statistical models and predictive algorithms to support workforce planning, forecasting, and program evaluation. • Develop and maintain dashboards, reports, and data visualizations that communicate actionable insights to leadership and stakeholders. • Conduct ad hoc analyses to address emerging workforce and training requirements and produce rapid-turn analytical products. • Advise on data governance, privacy requirements, ethical data use, and documentation standards. • Collaborate with staff and stakeholders to define analytic requirements, measures of effectiveness, and reporting cadence. • Deliverables (As Requested): Cleaned datasets, data dictionaries, and repeatable analytic workflows. Workforce/training metrics and performance measures (definitions and calculation logic). Predictive models/forecasts and concise model documentation (assumptions, validation, limitations). Dashboards and recurring reports for leadership and program stakeholders. Ad hoc analytic briefs, slides, and decision-support memos. Recommendations for data governance and privacy/ethical data practices.

• Partnering with teams across Canva to influence strategic decisions through data. • Designing and analysing A/B tests and other experiments that drive product and business outcomes. • Building data models, dashboards, and tooling that help teams scale insights. • Defining and forecasting key metrics to track progress and surface opportunities. • Communicating findings clearly to a wide range of technical and non-technical audiences. • Uplifting the broader data community through mentorship, best practices, and collaboration.

• Lead the development and deployment of sophisticated analytics models to predict, quantify, and interpret complex data related to mortgage lending. • Utilize machine learning, statistical analysis, and other advanced techniques to improve decision-making processes, risk assessment, and customer segmentation. • Work closely with business units to identify opportunities for leveraging company data to drive business solutions. • Innovate and implement new modeling techniques and algorithms for predictive analytics and data mining. • Act as a thought leader, staying abreast of the latest industry trends and technologies in data science. • Apply advanced machine learning techniques and algorithms to analyze large and complex data sets, such as supervised and unsupervised learning, deep learning, natural language processing, computer vision, recommender systems, anomaly detection, etc. • Ensure the accuracy and integrity of data used for analysis. • Implement data collection systems and other strategies that optimize statistical efficiency and data quality. • Collaborate with data engineering teams to improve data collection, storage, processing, and security practices. • Partner with IT, software development, and business teams to translate business needs into technical specifications. • Design and build data-driven products and services that enhance the customer experience and contribute to the bottom [{not fully visible in given text}]. • Facilitate the integration of analytics and machine learning capabilities into the company’s operations and decision-making processes. • Develop and present clear, comprehensive reports and visualizations for both technical and non-technical audiences. • Communicate complex analytical results and insights in a manner that is easily understandable, facilitating informed decision-making across the company. • Provide guidance and mentorship to junior data scientists and lead project teams, ensuring the timely and successful completion of projects. • Foster a culture of innovation, continuous learning, and collaboration within the team. • Identify, track, and monitor trends and avoidable technology-related errors; work across functions to develop complex solutions, improvements, and stop-gaps. • Focus on the continual improvement of policies, procedures, and processes falling under the scope of authority. • Use expertise to resolve high level issues that cannot be solved by teammates. • Stay abreast of latest technology trends and participate in high-level decisions impacting the direction of the Information Technology function. • Partner with the DevOps team to setup scalable MLOps pipelines. • Perform other duties as assigned.