Data Product Analyst; Data Science

Adastra is seeking a Data Product Analyst (Data Science) to serve as a critical bridge between business stakeholders, data science and analytics teams, and data management groups. This role combines strong data analysis and data science capabilities with core business analysis skills to ensure that data is effectively leveraged to inform strategic decision making, improve processes, and influence solution design.

You will frame business problems as analytical and modeling questions, apply statistical and machine learning methods to answer them, and translate the results into clear, actionable recommendations. In addition to championing data stewardship, the Data Product Analyst plays a key role in eliciting requirements, documenting business processes, and facilitating alignment across technical and business teams.

Primary

Location:

Across Canada

Work Model: Fully Remote

Employment Type: Contractor

Vacancy Status: New

• Act as the Data Subject Matter Expert to stakeholders with an understanding of data across the end-to-end data supply chain
• Translate business problems into well-scoped analytical and data science use cases, working with stakeholders and technical leads to define requirements, success metrics, and approach
• Collect, clean, and analyze data from multiple structured and unstructured sources to uncover trends, patterns, and actionable insights
• Apply statistical analysis and experimentation (e.g., A/B testing) and develop predictive and descriptive models (e.g., segmentation, classification, forecasting, anomaly detection) in partnership with data science and engineering teams, evaluating performance with attention to bias, data leakage, and overfitting
• Translate analytical and modeling findings into clear, business-friendly insights
• Elicit, document, and manage business, functional, and non-functional requirements through techniques such as interviews, workshops, process analysis, and user story creation
• Support prioritization by assessing business value, impacts, complexity, and risk, and ensure traceability of requirements from intake through development, testing, and deployment
• Collaborate with data engineers, architects, data scientists, and MLOps teams to ensure solutions reflect business context and use cases, and to move analytical work from prototype toward production
• Define testing acceptance criteria, support user validation, and provide structured feedback for continuous improvement
• Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities

• Bachelor’s degree in information systems, business analysis, statistics, computer science, data science, or related fields
• 2-5 years of experience in data analysis, data science, business analysis, product analytics, or similar roles
• Skilled at requirements elicitation, process mapping, and translating business needs into technical specifications, user stories, acceptance criteria, and high-quality documentation
• Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use, and the ability to adapt these skills across a wide range of use cases
• Comfortable connecting data across multiple systems and enriching datasets to enable new insights for business stakeholders
• Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders, including explaining model approaches and trade-offs
• Champions a consumer-first mindset, grounding product, data, and insights decisions in a deep understanding of consumer behaviors and needs
• Collaborative mindset with the ability to influence without authority, navigate ambiguity, and drive alignment across cross-functional teams
• Naturally curious and a strong critical thinker, with a passion for exploring new data sources and solving problems with incomplete, messy, or evolving datasets
• Strong analytical toolkit leveraging SQL, Python (e.g., pandas, Num Py, scikit-learn), and BI tools (e.g., Power BI, Tableau) to explore, transform, model, and validate complex datasets
• Solid grounding in statistics, model evaluation, and validation practices, with working knowledge of machine learning methods (e.g., regression, tree-based models, time-series forecasting) and exposure to generative AI and LLM-based tooling
• Working knowledge of data modeling, ETL/ELT pipelines, and cloud data platforms (e.g.,…