What You'll Do
Take ownership of designing and refining machine learning models and the systems that train them, using extensive internal datasets to generate meaningful features. Translate business challenges into data-driven solutions by working closely with product, live operations, and technical teams. Help shape the architecture of AI-powered services and see them through from design to deployment and ongoing maintenance. On occasion, build intuitive web-based tools that allow non-technical users to interact with model outputs and make informed decisions.
Requirements
- Minimum of three years of experience in a data scientist or research-oriented machine learning engineering role
- Demonstrated ability to apply statistical and machine learning methods to practical business problems
- Hands-on experience developing complete model pipelines, including feature engineering and training workflows
- Strong fluency in Python and widely used data science libraries and frameworks
- Experience handling both structured and unstructured data at scale
- Clear understanding of how models evolve from development to deployment and monitoring in production
- Proficient in spoken and written English
Preferred Qualifications
- Advanced degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, or Data Science
- Background in deep learning applications for image analysis or generation
- Experience analyzing time series data and building forecasting models
- Work with natural language processing techniques
- Familiarity with Snowflake as a data warehouse and platform for machine learning workflows
- Experience using data visualization platforms like Tableau or Power BI
- Knowledge of Python-based interactive tools such as Dash or Streamlit
- Cloud platform experience, particularly AWS services including SageMaker, EC2, Fargate, and ECS
- Working knowledge of Docker, Git, and continuous integration and delivery pipelines
