We are looking for a curious, analytical, and technically strong Senior Data and ML/AI Scientist to help design and build AI-powered solutions that enhance our B2B SaaS platform. As an individual contributor, you will develop machine learning models, data-driven insights, and AI prototypes that directly impact customer experience, automation, and business outcomes. In this role, you will work alongside AI engineers, product teams, and data engineers to bring intelligent features to life-leveraging industry-leading tools and frameworks to ensure models are accurate, scalable, and production-ready. What you'll do: 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 What you'll bring: • 4-6 years of experience in data science, ML engineering, or applied AI roles, ideally within a SaaS or tech product environment. • Experience building customer-facing or business-impacting models (e.g., scoring, personalization, anomaly detection). • 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. • Experience designing or evaluating models for robustness, interpretability, and inclusivity is a plus. 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