Senior Data and AI Scientist

Role overview

Model development and analysis

• Design machine-learning and statistical models for recommendations, predictions, and classification.
• Explore data, engineer features, and preprocess datasets for training and evaluation.
• Analyze model performance and iterate through experimentation.
• Collaborate with analysts and product stakeholders on use cases and success criteria.
• Prototype and benchmark algorithms in NLP, representation learning, and time-series forecasting.

Insights and decision support

• Generate actionable insights through analytics, segmentation, and statistical analysis.
• Communicate findings and model outcomes to cross-functional teams.
• Contribute to A/B testing and model validation.
• Develop dashboards or monitoring tools to track model health and business KPIs after deployment.

Collaboration and technical delivery

• Partner with product and AI engineering teams to deploy models and integrate with backend services and APIs.
• Build reproducible training pipelines and notebooks using versioning and documentation standards.
• Contribute reusable model components, feature stores, internal tooling, and documentation.
• Keep current with data and ML/AI tools and frameworks.

Qualifications and experience

• 4–6 years in data science, ML engineering, or applied AI, ideally in SaaS or a technology product environment.
• Experience building customer-facing or business-impacting models, such as scoring, personalization, or anomaly detection.
• Familiarity with Git, code reviews, and CI/CD for ML.
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related field.
• Able to work independently and contribute to a team; strong communication skills for technical and non-technical audiences.
• Awareness of fairness, bias, and explainability in ML; robust, interpretable, and inclusive model experience is a plus.

Technical skills

• Languages and tools include Python, SQL, Jupyter, and Git.
• ML frameworks include scikit-learn, XGBoost, LightGBM, and TensorFlow or PyTorch at a basic level.
• Data tools include Pandas, NumPy, DBT, BigQuery, Snowflake, or similar.
• MLOps tools include MLflow, Airflow, Kubeflow, and optional feature stores such as Tecton or Feast.
• Experience with one cloud ML platform is required: GCP Vertex AI, AWS SageMaker, or Azure ML.
• Docker or Kubernetes is a plus; visualization tools include Seaborn, Matplotlib, Plotly, with BI familiarity a plus.
• Strong understanding of evaluation, overfitting, bias, variance, cross-validation, and A/B testing.
• Advanced exposure to deep learning, graph ML, NLP, or reinforcement learning is an asset.
• Production model deployment and monitoring experience, including retraining, logging, and performance tracking, is highly valued.

Work arrangement

• Benevity describes its approach as flexible hybrid, with no set office attendance requirement for people near an office.