Senior Data Scientist

Toast · Enterprise · Bangalore, India · R & D : Engineering : Fintech Data Science & AI

Senior Data Scientist at Toast, focusing on end-to-end development and production deployment of agentic AI workflows and ML solutions within the fintech domain. Requires strong engineering mindset, Python/SQL proficiency, and experience with modern ML techniques.

What you'd actually do

  1. Demonstrated experience in designing and deploying agentic AI workflows and ML/DL solutions in production, with a strong focus on reliability and scalability.
  2. Independently lead the end-to-end lifecycle of machine learning solutions—from problem framing and model development to deployment and impact measurement
  3. Collaborate closely with Product, Data Science, ML Engineering, and Platform teams to translate business problems into agent-driven solutions
  4. Decompose ambiguous, large-scale problems into structured, iterative deliverables that drive incremental and measurable business value.
  5. Maintain high standards of code quality, documentation, and reproducibility, enabling scalability and knowledge sharing across the team

Skills

Required

  • Python
  • SQL
  • Spark
  • scikit-learn
  • TensorFlow
  • PyTorch
  • supervised and unsupervised learning
  • classification
  • regression
  • clustering
  • segmentation
  • anomaly detection
  • predictive modeling
  • ensemble and boosting methods
  • statistical modeling
  • feature engineering
  • optimization
  • model evaluation
  • deep learning
  • time series forecasting
  • recommendation systems
  • structured and unstructured data handling
  • agentic AI systems
  • object-oriented design
  • testing (TDD)
  • CI/CD
  • version control (Git)
  • workflow orchestration (Airflow, Luigi)
  • AWS
  • SageMaker
  • ECS
  • DynamoDB
  • cross-functional collaboration
  • analytical mindset
  • communication

Nice to have

  • XGBoost
  • LightGBM
  • CatBoost
  • research
  • curiosity

What the JD emphasized

  • agentic AI workflows
  • end-to-end lifecycle of machine learning solutions
  • agent-driven solutions
  • structured, iterative deliverables
  • code quality, documentation, and reproducibility
  • agentic or autonomous AI systems in real-world production environments

Other signals

  • agentic AI workflows
  • end-to-end development of machine learning solutions
  • production-quality code
  • deploy robust, reliable models
  • monitored, and continuously improved