- Company Name
- Square
- Job Title
- Machine Learning Engineer (Modeling), Risk
- Job Description
-
**Job Title**
Machine Learning Engineer (Modeling), Risk
**Role Summary**
Design, develop, and deploy machine learning models that detect and prevent fraud in large‑scale financial ecosystems. Collaborate across product, engineering, policy, and operations to transform data into actionable risk insights, ensuring secure and seamless user experiences.
**Expectations**
- Lead end‑to‑end ML lifecycle: data ingestion, feature engineering, model training, validation, deployment, and monitoring.
- Serve as a risk domain expert, shaping strategy for modeling initiatives and influencing product roadmap.
- Mentor junior teammates, provide constructive feedback, and foster a culture of collaboration and continuous improvement.
**Key Responsibilities**
- Partner with cross‑functional teams to prioritize and scope ML projects that address chargeback and fraud risks.
- Build and productionize scalable ML solutions using tree‑based models, deep learning, and reinforcement learning.
- Leverage modern ML stacks (PySpark, PyTorch, MLflow, GCP Vertex AI) and orchestration tools (Airflow, Prefect) to streamline model pipelines.
- Deploy models on cloud platforms (AWS, GCP), ensuring robust CI/CD practices and containerization for reproducibility.
- Integrate third‑party data sources to enhance model performance and accuracy.
- Monitor production models, analyze drift, and iterate for continual improvement.
- Contribute to tooling and process improvements that accelerate ML development and increase model effectiveness.
- Communicate findings and model insights to stakeholders in an accessible manner.
**Required Skills**
- 3+ years of machine learning engineering experience.
- Strong background in designing, deploying, and troubleshooting ML solutions (tree‑based models, deep learning, transfer learning, reinforcement learning).
- Proficiency with PySpark, PyTorch, MLflow, GCP Vertex AI.
- Experience with workflow orchestration (Airflow, Prefect) and containerization (Docker).
- Comfortable with cloud platforms (AWS, GCP) and CI/CD pipelines.
- Excellent communication and collaboration with cross‑functional teams.
- Ability to mentor and provide feedback to teammates.
**Required Education & Certifications**
- Bachelor’s or graduate degree in Computer Science, Engineering, Statistics, Physics, Applied Mathematics, or a related technical field.
- No mandatory certifications required, though relevant machine learning or cloud certifications are a plus.