Job Title

Reporting to Lead, Data Scientist, the Data Engineer-Scientist role is to harness vast amounts of data to optimize business results. He/she will exercise their knowledge of descriptive and multivariate statistical techniques and applications, and database analysis tools and techniques to develop strategic insights to drive business goals. Data scientists are more excited about what they can learn with data than they are about what is already known. The data scientist works hard at advancing the state-of-the-art in how data is applied to solve problems in our industry. They are also responsible for teaching data analysts and software engineers how to work effectively with vast quantities of data. We are seeking a versatile and driven Data Engineer-Scientist to join our team. This hybrid role combines the technical expertise of a data engineer with the analytical and modeling skills of a data scientist. The successful candidate will work across the entire data value stream, from ingestion and transformation to advanced analytics and predictive modeling, ensuring seamless integration of data-driven insights into our business processes. RESPONSIBILITIES Data and Project Ownership: Assisting in the data design, execution, and delivery of high-impact data projects, aligning with business goals and advancing our Circular Materials initiatives. Advanced Data Science & Data Modeling: Apply advanced machine learning and statistical techniques to solve complex business problems. Enhance existing models and develop new predictive and prescriptive analytics solutions to uncover actionable insights. Advanced knowledge of statistical modeling and machine learning techniques, including regression, classification, clustering, and time series analysis. Design, build, and optimize scalable and reliable data pipelines to ingest, process, and store structured and unstructured data. Develop and maintain ETL/ELT processes for the data lake and data warehouse (e.g., AWS Redshift, Snowflake). Ensure data integrity, security, and governance by implementing best practices and adhering to compliance standards. Provide inputs to data dictionaries and data design. Collaborate with DevOps to deploy and monitor data solutions in production environments. Data Science: Analyze complex datasets to extract meaningful insights and identify trends that drive business outcomes. Develop and deploy machine learning models to solve key business challenges, such as predictive analytics, recommendation systems, or anomaly detection. Create visualizations, dashboards, and reports to communicate findings to stakeholders effectively. Conduct hypothesis testing, A/B experiments, and statistical analysis to support decision-making. Cross-Functional Collaboration: Act as a bridge between engineering and analytics teams, ensuring smooth data flow and integration. Collaborate with product managers, analysts, and other stakeholders to understand requirements and align data solutions with business objectives. Proactively identify gaps in data systems, models, or analytics processes and implement enhancements. Stakeholder Engagement: Partner closely with business stakeholders and teams to understand their needs, translate them into technical solutions, and deliver insights that drive strategic decisions. Innovation & Process Improvement: Identify areas for innovation and improvement in tools, techniques, and processes. Champion best practices for data quality, integration, and governance across the organization. Utilize advanced statistical, machine learning, and AI techniques to analyze complex datasets and build predictive models that drive actionable insights for the organization. Business Intelligence & Visualization: Assist in the development of advanced dashboards, reports, and visualizations to make insights easily accessible to decision-makers. Create data mining and analytics architectures, coding standards, stats reporting and data analysis methodologies. Establish links across existing data sources and find new, interesting mash-ups. Coordinate data resource requirements between analytics teams and engineering teams. Operational Management: Develop algorithms and predictive models to solve critical business problems. Develop tools and libraries that will help analytics team members more efficiently interface with huge amounts of data. Analyze large, noisy datasets and identify meaningful patterns that provide actionable results. Develop and automate new enhanced imputation algorithms. Create informative visualizations that intuitively display large amounts of data and/or complex relationships. Provide and apply quality assurance best practices for data science services across the organization. Develop, implement, and maintain change control and testing processes for modifications to algorithms and data analytics. Ensure effective protection and integrity of data assets. QUALIFICATIONS Education and experience: Bachelors degree in a relevant field (e.g., computer science, mathematics, engineering, statistics); a Masters degree is a plus. 5+ years in data science/data engineering, analytics, or a related field, with proven expertise in developing impactful data science solutions. Proven Expertise: A strong track record of success as a Data Scientist within organizations, with a history of delivering impactful insights and solutions. In-depth experience with tools like Python, SQL, and Power BI, along with a solid foundation in statistical modeling and machine learning techniques. Familiarity with sustainability and Circular Materials initiatives. Experience with automation and AI tools. Agile methodologies and JIRA is a plus. Leadership Potential: Demonstrated ability to take initiative, lead projects, and collaborate effectively with team members and stakeholders across the organization. Effective Communication: Exceptional skills in storytelling and presenting complex data insights in a way that resonates with diverse audiences, including senior leadership and stakeholders. Problem-Solving Skills: A knack for creative problem-solving, with the ability to devise elegant solutions to challenging technical and business problems. WORKING CONDITIONS Working Hours: All CM employees work 40 hours per week, remotely from a home office environment. Extra or flexible hours may be required on occasion. Travel: Not applicable. ABOUT CIRCULAR MATERIALS Circular Materials is a national producer-led not-for-profit organization that supports producers in meeting their obligations under extended producer responsibility regulations across Canada. We are committed to building efficient and effective recycling systems to minimize waste and ensure materials are reused again and again. Through the support of our founders and member producers, Circular Materials is implementing a national harmonized approach focused on improving recovery rates, meeting the needs and expectations of consumers, and protecting the natural environment. Think you would be a good fit for our Data Engineer - Scientist position? We want to hear from you! We thank all applicants for their interest. However, only those under consideration will be contacted. Circular Materials is an equal opportunity employer. In accordance with the Accessible Canada Act, 2019, and all applicable provincial accessibility standards, accommodation will be provided by Circular Materials throughout the recruitment, selection and/or assessment process to applicants with disabilities, upon request by emailing to . This email is only used for accommodation requests. #J-18808-Ljbffr