Data Scientist

Actlabs is a global leader in laboratory analytical services with 35+ years of experience across mining, geochemistry, environmental testing, agriculture, cannabis, health, life sciences, and petroleum industries. We are building a dedicated AI and Data Science team to unlock the value of our extensive data asset and drive competitive intelligence, operational efficiency, and client value.

We are investing in the future, implementing innovative strategies to maintain our leadership and strengthen our impact across the sectors we serve. This is an exciting opportunity for dynamic individuals who want to be part of an established and rapidly growing company.

The Data Scientist works within an agile, cross‑functional team to deliver data‑driven initiatives from insight to impact. You will own the analytical and insight layer, statistical interpretation, storytelling with data, and stakeholder communication, while collaborating with teammates across data engineering and technical implementation. You will bridge the gap between analytical outputs and operational decision‑makers, translating complex findings into clear, defensible recommendations.

Master's or PhD in Statistics, Mathematics, Data Science, Computer Science, Physics, or a related quantitative field strongly preferred;
Bachelor's considered with exceptional applied experience.

• 5+ years of hands‑on data science experience, including 2+ years delivering models to production with documented business impact
• Proven experience with complex, real‑world datasets and communicating findings to non‑technical stakeholders

• Experience with laboratory, scientific, industrial, or quality control data
• Familiarity with LLM fundamentals or NLP applications
• MLOps knowledge: model tracking, registries, and monitoring frameworks
• Background in regulated or accredited environments (ISO, GMP, or equivalent)
• Familiarity with geochemistry, mining, or environmental science domains

• Expert Python: pandas, Num Py, scikit‑learn, stats models, and visualization libraries
• Strong SQL for complex querying and large relational datasets
• Deep grounding in inferential statistics, hypothesis testing, regression, causal inference, and experimental design
• Practical experience with supervised and unsupervised learning, time series analysis, and anomaly detection
• A/B test design and analysis from first principles
• Proficiency in Power BI or equivalent (Tableau, Looker)
• Strong understanding of temporal validation and evaluation metrics for real‑world datasets

• Design, build, and validate statistical and machine learning models applied to complex, domain‑specific datasets
• Perform rigorous exploratory data analysis to surface patterns, outliers, and trends
• Apply causal inference and experimental methods to evaluate the impact of operational or process changes
• Translate model outputs into clear, actionable narratives for operational managers and senior leadership
• Design and execute experiments to validate hypotheses about processes, client behavior, and data quality

• Build and maintain predictive models across key business domains to support operational and commercial decision‑making
• Develop client intelligence models to inform retention, segmentation, and growth strategies
• Build and own Power BI dashboards that translate analytical findings into actionable business metrics
• Define core metric definitions and support pricing, capacity planning, and service optimization with quantitative analysis

• Develop deep familiarity with internal data flows, terminology, and the domain context behind the numbers
• Lead workshops with subject matter experts and commercial stakeholders to define ground truth for analytical tasks
• Collaborate with technical teammates on feature engineering and maintain reusable analytical datasets
• Identify and document data quality issues that affect analytical validity

• Own statistical validation including appropriate train/test design,…