Data Product Analyst; Data Science

Position: Data Product Analyst (Data Science)
Overview
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

RESPONSIBILITIES

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

QUALIFICATIONS, SKILLS & EXPERIENCE

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.,…