You will research, design, implement, train, deploy, and monitor machine learning algorithms that power data products. You will build analytical and training pipelines, automate experiments and model monitoring, collaborate with stakeholders to define machine learning problems, analyze complex datasets, and measure model performance and business impact.
Responsibilities
- Research, design, implement, train, deploy, and monitor machine learning algorithms
- Develop analytical and machine learning training pipelines
- Automate experiments, training runs, validation runs, and monitoring
- Collaborate with MLOps to improve machine learning tooling
- Work with product managers and business stakeholders to define machine learning tasks
- Analyze large and complex datasets to identify patterns and opportunities
- Define performance metrics and design experiments such as A/B tests
- Perform statistical analysis to validate model performance and quantify business impact
Requirements
- 5+ years of experience as a machine learning or data scientist
- Experience with the full machine learning model lifecycle in production
- Experience with big data frameworks and machine learning pipelines
- Understanding of software engineering, data engineering, and MLOps principles
- Knowledge of statistics, statistical inference, machine learning, and causal inference
- Familiarity with PySpark, Trino SQL, TensorFlow, PyTorch, XGBoost or LightGBM, Pandas, MLflow, and Airflow
- Experience designing and running experiments
- Ability to lead projects from ideation through deployment
- Ability to communicate complex outcomes to diverse audiences