Senior Data Scientist Engineer

Time Series, Forecasting, ML is important.

Key Responsibilities

• Develop deep learning frameworks for time series data.
• Work with identity domain experts to refactor heuristic logic into an algorithmic framework.
• Articulate trade-offs in model interpretability, generalization, and operational complexity, justifying the use of algorithmic models from both performance and deployment perspectives. Demonstrate where and why learned representations outperform the heuristic approach (e.g. in edge case detection, false-positive reduction, adaptive behavior over time).
• Architect and implement end-to-end machine learning solutions for digital identity resolution.
• Collaborate with engineering teams to ensure model scalability, reliability, and performance in production environments.
• Partner with product and operations teams to define success metrics and monitor model impact.
  

Qualifications

• 5+ years of experience in data science or machine learning roles.
• Proven experience in coding and delivering deep learning solutions for time series data.
• Strong proficiency in Python and SQL; experience with ML libraries such as TensorFlow/Keras, PyTorch/NeuralForecast, or aeon.
• Experience with cloud platforms (preferably AWS) and distributed computing frameworks
• Experience working with large datasets using EMR and/or Snowflake.
• Proven experience building and deploying deep learning models in production environments.
• Excellent communication skills and the ability to influence technical and non-technical stakeholders.
• Bachelor’s degree in computer science & engineering.
  

Preferred Qualifications

• Experience with Snowflake Cortex.
• Familiarity with MLOps practices and tools (e.g., MLflow, SageMaker, Airflow).