Manager, Data Science

LendingTree, LLC.•Charlotte, NC

47d•$180,000 - $210,000•Hybrid

About The Position

The Manager, Data Science will lead a team of data scientists to design, develop, and deploy models that drive measurable business outcomes across LendingTree. This role combines technical leadership with strategic oversight - ensuring scientific rigor, operational excellence, and cross-functional impact. You will play a key role in helping shape the team direction, mentoring talent, and collaborating with engineering, product, analytics, and business stakeholders to deliver scalable, high-quality data science/AI solutions. The ideal candidate is equally comfortable discussing model architectures, business tradeoffs, and team development strategies.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related field (PhD a plus).
7+ years of experience in applied data science, with experience in a leadership or people management role.
Proven ability to lead teams through full ML lifecycle - data preparation, modeling, validation, deployment, and monitoring.
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.
Strong analytical, organizational, and problem-solving skills with a track record of business impact.
Excellent written and verbal communication skills; capable of influencing technical and executive audiences.

Nice To Haves

Experience in fin-tech or other data-rich, high-scale consumer businesses.
Background in software engineering, model deployment, or data platform integration.
Experience managing hybrid teams (on-site and remote).
In depth knowledge and experience in leveraging GenAI & LLM capabilities, building Retrieval Augmented Generation/agentic workflows preferred

Responsibilities

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.
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.
Translate complex analytical findings into actionable business insights for diverse audiences.
Collaborate closely with Analytics, Product, and Platform leaders to integrate data-driven decision-making into products and operations.
Drive alignment across business units to ensure models address real-world needs and deliver measurable impact.