Senior Data and AI Scientist

About the Company

Responsibilities

• ● Model Development & Analysis
• ● Design and develop machine learning and statistical models to support product features such as recommendations, predictions, or classification.
• ● Conduct data exploration, feature engineering, and preprocessing to prepare datasets for model training and evaluation.
• ● Analyze model performance using appropriate metrics and iterate based on experimentation.
• ● Collaborate with data analysts and product stakeholders to understand use cases and define success criteria.
• ● Prototype and benchmark novel algorithms in areas such as NLP, representation learning, and time-series forecasting.
• ● Insights & Decision Support
• ● Generate actionable insights using advanced analytics, segmentation, and statistical analysis.
• ● Communicate findings and model outcomes to cross-functional teams in a clear and concise manner.
• ● Contribute to A/B testing and model validation efforts to ensure measurable impact.
• ● Develop dashboards or monitoring tools to track model health and business KPIs post-deployment.
• ● Collaboration & Technical Growth
• ● Work closely with product and AI engineering teams to deploy models into production environments, ensuring integration with backend services and APIs.
• ● Develop reproducible training pipelines and notebooks that adhere to versioning and documentation standards.
• ● Contribute to building reusable model components and feature stores that support scalable development.
• ● Stay up to date with advancements in the data and ML/AI ecosystem and explore new tools and frameworks
• ● Contribute to documentation and internal tooling that improve team efficiency and transparency

Requirements

• ● -6 years of experience in data science, ML engineering, or applied AI roles, ideally within a SaaS or tech product environment.
• ● Familiarity with collaborative software development workflows (e.g., Git, code reviews, CI/CD for ML).
• ● Ability to work independently on tasks while contributing effectively in team settings.
• ● Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or a related field.
• ● Strong communication skills with the ability to explain complex technical topics to both technical and non-technical audiences.
• ● Demonstrated awareness of ethical implications of ML models, including fairness, bias, and explainability in AI.
• ● Technical Skills & Expertise:
• ● Languages & Tools: Python, SQL, Jupyter, Git
• ● ML Frameworks: scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch (basic)
• ● Data Tools: Pandas, NumPy, DBT, BigQuery, Snowflake, or similar
• ● MLOps & Pipelines: MLflow, Airflow, Kubeflow, Feature Stores (Tecton, Feast, etc. optional but a plus)
• ● Cloud Platforms: GCP (Vertex AI), AWS (SageMaker), or Azure ML (experience with one required); experience with containerization (Docker) or orchestration (Kubernetes) is a plus
• ● Visualization & Reporting: Seaborn, Matplotlib, Plotly; BI tool familiarity (e.g., Looker, Power BI, Tableau) is a plus
• ● Model Evaluation & Experimentation: Strong grasp of model evaluation, overfitting, bias, variance, cross-validation, and A/B testing techniques
• ● Domain-Specific Knowledge: Exposure to advanced areas such as deep learning, graph ML, NLP, or reinforcement learning is a strong asset
• ● Production & Scalability Experience: Experience deploying and monitoring ML models in production, including automated retraining, logging, and performance tracking, is highly valued
• ● DISCOVER YOUR PURPOSE AT WORK
• ● We're not employees, we're Benevity-ites. From all locations, backgrounds and walks of life, who deserve more
• ● Innovative work. Growth opportunities. Caring co-workers. And a chance to do work that fills us with a sense of purpose.
• ● If the idea of working on tech that helps people do good in the world lights you up ... If you want a career where you're valued for who you are and challenged to see who you can become