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Benevity
Benevity
501 – 1,000 Employees
B2BSaaSSocial Impact
Benevity is an enterprise SaaS company focused on social impact. Its Benevity Enterprise Impact Platform helps large organizations manage employee giving, donation matching, payroll giving, volunteering, grants, employee groups, challenges, and impact reporting in one system. The platform also supports global disbursements, nonprofit due diligence, compliance, and risk controls, with AI-driven automation designed to simplify program administration and measurement. For professionals exploring roles in B2B software, SaaS, or social impact technology, Benevity offers the opportunity to work on products that connect workplace programs with charitable organizations and measurable community outcomes.
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Senior Data and AI Scientist — Hybrid in Canada

Senior Data and AI Scientist developing machine-learning models, analytics, and production AI capabilities for Benevity’s social-impact SaaS platform. The role focuses on improving customer experiences and delivering measurable business value.

CACanadaHybridFull-timeSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer
AirflowAWSAzureBigQueryCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

Description

  • Create machine-learning and statistical models for recommendations, predictions, and classification.
  • Perform data exploration, feature engineering, and preprocessing.
  • Evaluate model performance and refine solutions through experimentation.
  • Partner with data analysts and product stakeholders to establish use cases and success measures.
  • Prototype and compare methods in NLP, representation learning, and time-series forecasting.
  • Produce actionable insights using advanced analytics, segmentation, and statistical methods.
  • Present findings and model results to cross-functional teams.
  • Support A/B testing and model validation initiatives.
  • Create dashboards and monitoring tools for model health and business KPIs.
  • Collaborate with product and AI engineering teams to bring models into production.
  • Build reproducible training pipelines and notebooks.
  • Develop reusable model components and feature stores.
  • Track developments across the data, machine learning, and AI ecosystem.
  • Support documentation and internal tooling.

Requirements

  • 4–6 years of experience in data science, machine learning engineering, or applied AI.
  • Experience in a SaaS or technology product setting is preferred.
  • Background building models that serve customers or produce business impact.
  • Working knowledge of Git, code reviews, and CI/CD practices for machine learning.
  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related discipline.
  • Proficiency with Python, SQL, Jupyter, and Git.
  • Experience with scikit-learn, XGBoost, LightGBM, and foundational TensorFlow or PyTorch.
  • Familiarity with Pandas, NumPy, DBT, BigQuery, Snowflake, or comparable tools.
  • Experience using MLflow and Airflow.
  • Experience with at least one cloud environment: GCP/Vertex AI, AWS/SageMaker, or Azure ML.
  • Strong understanding of model evaluation, overfitting, bias, variance, cross-validation, and A/B testing.
  • Clear and effective communication skills.
  • Understanding of ethical considerations in machine learning, including fairness, bias, and explainability.
  • Production experience deploying and monitoring machine-learning models is highly valued.

Benefits

  • Flexible hybrid working arrangements without a fixed in-office attendance requirement.
  • Opportunities to contribute to innovative work.
  • Support for professional growth.
  • Collaborative and caring colleagues.
  • Purpose-driven work.
  • Employee resource groups.
  • Accessible hiring practices and accommodations for candidates with disabilities.

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