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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 at Benevity — Toronto Hybrid

Senior Data and AI Scientist developing machine-learning models, analytics, and AI prototypes for Benevity’s social-impact SaaS platform. The role focuses on production models, customer experience, and automation.

CACanada
$109,400 – $150,370 / year
HybridFull-timeSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer
AirflowAWSAzureBigQueryCloudDockerGoogle Cloud PlatformKubernetesNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

Description

  • Build machine-learning and statistical models for recommendations, predictions, and classification
  • Explore data, engineer features, and preprocess datasets for model training and evaluation
  • Evaluate model performance with appropriate metrics and refine approaches through experimentation
  • Partner with data analysts and product stakeholders to clarify use cases and establish success criteria
  • Prototype and benchmark methods in NLP, representation learning, and time-series forecasting
  • Produce actionable insights through advanced analytics, segmentation, and statistical analysis
  • Present findings and model results to cross-functional teams
  • Support A/B testing and model validation activities
  • Create dashboards and monitoring tools for post-deployment model health and business KPIs
  • Partner with product and AI engineering teams to deploy models and connect them with backend services and APIs
  • Build reproducible training pipelines and notebooks using versioning and documentation standards
  • Help develop reusable model components and feature stores
  • Monitor developments across the data, machine-learning, and AI ecosystem and assess new tools and frameworks
  • Improve team efficiency and transparency through documentation and internal tooling

Requirements

  • 4–6 years of experience in data science, machine-learning engineering, or applied AI, preferably in a SaaS or technology product environment
  • Experience developing customer-facing or business-impacting models, including scoring, personalization, or anomaly detection
  • Familiarity with collaborative software development practices such as Git, code review, and CI/CD 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 basic TensorFlow or PyTorch
  • Experience with Pandas, NumPy, DBT, BigQuery, Snowflake, or comparable data tools
  • Experience with MLflow and Airflow
  • Experience using at least one cloud platform: GCP/Vertex AI, AWS/SageMaker, or Azure ML
  • Strong understanding of model evaluation, overfitting, bias, variance, cross-validation, and A/B testing
  • Experience assessing model robustness, interpretability, and inclusivity is an advantage
  • Docker or Kubernetes experience is an advantage
  • Experience with feature stores such as Tecton or Feast is optional but beneficial
  • Exposure to deep learning, graph machine learning, NLP, or reinforcement learning is a strong asset
  • Production experience deploying and monitoring machine-learning models, including automated retraining, logging, and performance tracking, is highly valued
  • Strong communication skills, including the ability to explain complex technical topics to technical and non-technical audiences
  • Awareness of ethical considerations in machine learning, including fairness, bias, and AI explainability

Benefits

  • Flexible hybrid working arrangements
  • No fixed in-office attendance requirement for employees located near an office
  • Opportunities to work on innovative projects
  • Professional growth opportunities
  • Supportive colleagues
  • Purpose-driven work
  • Employee resource groups
  • Equal opportunity for all candidates and employees
  • Accommodations throughout the hiring and assessment process for candidates with disabilities

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