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Adastra

Data, cloud, and AI consulting

Data Product Analyst - Data Science

Data ScienceConfirmed live in the last 24 hours
Full-timeRemote in CanadaNo salary listed

About the Job

Requirements

  • A bachelor's degree in information systems, business analysis, statistics, computer science, data science, or a related field.
  • Two to five years of experience in data analysis, data science, business analysis, product analytics, or similar roles.
  • Skill in requirements elicitation, process mapping, and translating business needs into technical specifications, user stories, acceptance criteria, and high-quality documentation.
  • A strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use, with the ability to adapt these skills across varied use cases.
  • Ability to connect data across multiple systems and enrich datasets to enable new insights for business stakeholders.
  • Ability to explain model approaches and trade-offs to technical teams and business stakeholders.
  • Ability to ground product, data, and insights decisions in a deep understanding of consumer behaviors and needs.
  • Ability to influence without authority, navigate ambiguity, and drive alignment across cross-functional teams.
  • Critical-thinking ability and interest in exploring new data sources and solving problems with incomplete, messy, or evolving datasets.
  • Strong analytical toolkit using SQL, Python, pandas, NumPy, scikit-learn, and business intelligence tools such as Power BI or Tableau to explore, transform, model, and validate complex datasets.
  • A solid grounding in statistics, model evaluation, and validation practices; working knowledge of machine learning methods such as regression, tree-based models, and time-series forecasting; and exposure to generative artificial intelligence and large language model tooling.
  • Working knowledge of data modeling, extract/transform/load and extract/load/transform pipelines, cloud data platforms such as Snowflake, Databricks, or AWS, and Git-based version control.
  • Ability to support delivery of data products, including analytical and machine-learning-driven products, in cross-functional environments and for multiple stakeholders.

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 an approach.
  • Collect, clean, and analyze data from multiple structured and unstructured sources to uncover trends, patterns, and actionable insights.
  • Apply statistical analysis and experimentation, including A/B testing, and develop predictive and descriptive models such as segmentation, classification, forecasting, and anomaly detection in partnership with data science and engineering teams; evaluate 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 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.

Desired Qualifications

  • A master's degree in a quantitative field.
  • Experience building and evaluating machine learning models.
  • Familiarity with experimentation design, responsible artificial intelligence, and model explainability.

About the Company

Adastra

Data, cloud, and AI consulting

Adastra is a data and digital transformation consultancy. It helps organizations design, build, migrate, govern, and operate data, analytics, cloud, artificial intelligence, and software solutions. Its clients include enterprises and public organizations modernizing information platforms and data-driven business processes. The consultancy delivers through multidisciplinary teams spanning strategy, architecture, engineering, governance, implementation, quality, project management, and managed support. Work spans data engineering, cloud, analytics, AI, governance, software, consulting, and project delivery.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Bangkok, Thailand
Founded
2018

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