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Oakville, Ontario, Canada
Hybrid
We are seeking a versatile and highly skilled Data Scientist to join our dynamic team. This role is pivotal in driving data-driven decision-making across our B2B initiatives, with occasional contributions to our B2C sector and Adtech initiatives. You will be responsible for developing sophisticated models, conducting deep-dive analyses, and extracting actionable insights from complex datasets to optimize user experience and advertising performance.
This is a unique opportunity to work at the intersection of B2B weather intelligence and commercial data products, influencing the direction of our predictive algorithms under the mentorship of the Team Lead of Data Science working closely with leadership to align data strategies with business objectives. We offer a collaborative environment where innovation is encouraged and data-driven insights are at the heart of everything we do.
Please note, we are only considering candidates who are eligible to work in Canada and can work in a Hybrid model at our Oakville locationBuild Predictive Weather Models: You will design, train, and deploy machine learning and spatio-temporal models to integrate weather feeds with client sales data to predicting demand, optimizing forecasts, and quantifying operational risk.
Derive Actionable Insights: You will conduct end-to-end exploratory analysis on complex meteorological datasets to uncover high-value patterns and translate probabilistic trends into strategic recommendations.
Drive B2B & Commercial Growth: You will partner with Sales, Marketing, and Customer Support to build analytics solutions for B2B initiatives.
Optimize ML Performance: You will leverage distributed frameworks (e.g., PySpark, Dask, Ray) and modern ML stacks (Scikit-learn, PyTorch/TensorFlow, LLMs) to improve model speed, scale inference pipelines, and lower compute costs.
Own the Model Lifecycle: Take responsibility for the full end-to-end lifecycle, including testing, deployment, and ongoing monitoring to ensure production-grade stability and reliability.
Technical documentation: Actively document model architectures, data lineage, and decision logic to ensure reproducibility, facilitate peer review, and maintain long-term institutional knowledge.
Cross-Functional Collaboration: You will partner closely with Data Engineers, Product Managers, and Revenue leaders to embed predictive algorithms into live APIs and deliver scalable data products that solve real-world enterprise problems.
Degree in a STEM discipline (Data Science, Statistics, Computer Science, or a related quantitative field); a Master’s or PhD is preferred but not required.
3+ years of data science experience specializing in B2B environments, with expertise in spatio-temporal modeling, time-series forecasting, and weather data intelligence.
High proficiency in Google BigQuery (SQL, window functions, query optimization, and large-scale data manipulation).
Advanced expertise in Python for statistical modeling, data science, and graphical visualization (e.g., Matplotlib, Seaborn, or Plotly), with hands-on experience using core ML libraries including NumPy, SciPy, Scikit-learn, and TensorFlow or PyTorch.
Experience working with distributed computing frameworks (e.g., Apache Spark) to process large-scale datasets.
Strong communication skills, including the ability to explain complex ideas to an audience with a non-technical background
Proven experience developing and deploying Deep Learning models in industry or research environments.
Familiarity with deploying Large Language Models (LLMs) or retrieval-augmented generation (RAG) pipelines for automated reporting, intelligent customer support routing, or unstructured data processing.
Experience scaling models across large datasets using distributed processing frameworks.