At DICK’S Sporting Goods, our people-centric approach puts our Athletes (customers) and Teammates (employees) at the heart of every decision, leading to transformational experiences in sport, online, and in-store. Technology at DICK’S is a collaborative and innovative environment where we solve real business problems and empower each other to excel. Our Technology Teammates win together by building innovative solutions to interesting business problems. As a Machine Learning Engineer II, you will design, build, and deploy advanced machine learning systems and AI applications that drive business impact. Your work will emphasize productionalizing causal inference and Bayesian modeling solutions.
Requirements
- Expert understanding of Python and experience with ML and deep learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Knowledge of software engineering principles, including secure and reliable software development.
- Experience with model deployment tools (MLFlow, Docker, Kubernetes, FastAPI).
- Experience with data versioning tools and experiment tracking (Weights & Biases, MLFlow).
- Familiarity with causal inference libraries (EconML, DoWhy, CausalML) is a plus.
- Experience working with media data is a plus.
- Ability to analyze and optimize model performance, reliability, and scalability.
Responsibilities
- Design and develop machine learning architecture and model deployment pipelines for batch and streaming use cases, integrating traditional ML models with causal inference methods.
- Optimize and improve the performance of existing ML models and systems, ensuring scalability, reliability, and efficiency.
- Leverage cloud deployment architecture for deploying ML and causal inference models as APIs for real-time inference with caching.
- Develop and maintain APIs for ML models to facilitate integration with other systems and applications.
- Collaborate closely with the ML Platform team to develop and maintain the ML Platform to meet business and Technology objectives using cutting edge tools and techniques.
- Collaborate closely with data scientists and engineers to validate and ensure data quality in production data.
- Develop solutions to monitor and address model drift, performance degradation, and assumption violations in deployed models.
Other
- 3+ years of experience in machine learning engineering and/or data science strongly preferred.
- Hands-on experience developing, deploying, and maintaining machine learning models and pipelines in production environments.
- Strong communication skills for collaborating with cross-functional teams and documenting technical work.
- Cameras must be on during all virtual interviews.
- AI tools are not permitted to be used by the candidateduring any part of the interview process.
