LendingTree is looking to hire a Manager, Data Science to lead a team in designing, developing, and deploying models that drive measurable business outcomes across the company. This role aims to shape team direction, mentor talent, and deliver scalable, high-quality data science/AI solutions by combining technical leadership with strategic oversight.
Requirements
- Advanced proficiency in Python, SQL, and data science libraries (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
- Experience with cloud-based ML platforms (AWS SageMaker or Snowpark preferred).
- Solid understanding of ML Ops, reproducibility, and governance practices.
- Proven ability to lead teams through full ML lifecycle — data preparation, modeling, validation, deployment, and monitoring.
- In depth knowledge and experience in leveraging GenAI & LLM capabilities, building Retrieval Augmented Generation/agentic workflows preferred
- Background in software engineering, model deployment, or data platform integration.
- Experience in fin-tech or other data-rich, high-scale consumer businesses.
Responsibilities
- Oversee the design, development, and deployment of data science models, ensuring scalability, reproducibility, and operational performance.
- Guide the team through data acquisition, feature engineering, and model lifecycle management from prototype to production.
- Partner with MLOps and engineering to streamline workflows and monitor models in production environments.
- Review and enhance model documentation, testing, and versioning standards.
- Apply expertise in Python, SQL, and ML frameworks (Scikit-learn, PyTorch, TensorFlow, etc.) to provide hands-on guidance where needed.
- Lead code reviews and establish quality control standards for data science deliverables.
- Champion explainability, fairness, and reliability in all model-driven solutions.
Other
- In-office presence is required three days a week.
- This position does not offer visa sponsorship.
- Lead, mentor, and develop a team of data scientists, fostering technical excellence and growth.
- Collaborate with senior stakeholders to identify and prioritize opportunities where machine learning and AI can deliver value.
- Promote best practices in experimentation, modeling, validation, and monitoring to ensure robust, production-grade solutions.
