Senior Data Scientist

LendingTree•Charlotte, NC
•$130,000 - $170,000•Hybrid

About The Position

The Senior Data Scientist leads complex analysis and model development projects, taking a hands-on approach to building predictive models, machine learning algorithms, and AI solutions that address business challenges and opportunities. The role helps shape and execute LendingTree’s data science roadmap, partners across teams to put advanced analytics into use, and provides guidance to junior teammates. The position is part of LendingTree’s Data Science team and works closely with engineering, product, business, finance, and analytics partners. It is based in Charlotte, North Carolina, and requires attendance at the Southend office three days a week. Candidates must be legally authorized to work in the United States; employment visa sponsorship is not available. Success includes developing reliable models for defined business needs, establishing effective validation and monitoring practices, communicating findings to support business decisions, building cross-functional partnerships, and helping junior teammates grow. The role is eligible for an annual performance bonus. Base pay is $130,000–$170,000 annually, varying based on internal factors and job-related knowledge, skills, and experience.

Requirements

  • Bachelor’s or master’s degree in Computer Science, Data Science, Statistics, or a related field.
  • At least five years of experience in a data science role.
  • Strong SQL and Python skills.
  • Strong data analysis, profiling, and statistics skills, with experience using SQL databases and analytical tools such as Oracle, SQL Server, MySQL, Pandas, NumPy, ggplot, Shiny, SciPy, scikit-learn, and Matplotlib.
  • Proficiency applying machine learning and deep learning algorithms, with experience using frameworks such as TensorFlow, PyTorch, scikit-learn, Spark ML, Torch, Hugging Face, Keras, Caffe, and CNTK.
  • Solid understanding of cloud computing and experience deploying models in environments such as AWS, Azure, or GCP.
  • Understanding of model development and willingness to explore deployment strategies and deepen experience bringing models into operational use.
  • Strong analytical and problem-solving skills, attention to detail, and ability to extract actionable insights from complex datasets.
  • Clear communication skills, including the ability to explain technical concepts to diverse audiences.
  • Must be legally authorized to work in the United States; employer visa sponsorship is not available.
  • Must attend the Southend office in Charlotte, NC three days a week.

Nice To Haves

  • Collaborative approach and ability to build strong relationships across technical and business teams.
  • Agility and composure when managing multiple projects in a fast-paced environment.
  • Curiosity about emerging data science methods and a proactive approach to learning and adapting.
  • Interest in mentoring junior teammates and improving how the team works.
  • Prior fintech or software engineering experience.
  • PhD in Computer Science, Data Science, Statistics, or a related field.

Responsibilities

  • Help execute data science strategies and roadmaps by translating business objectives into analytical solutions and AI/ML models.
  • Design, develop, and deploy predictive models, machine learning algorithms, and AI solutions.
  • Collaborate with teammates and business stakeholders to understand requirements, explore data, and develop models that deliver actionable insights.
  • Validate, test, and calibrate models to ensure validity, reliability, and robustness; monitor outcomes and refine performance.
  • Communicate complex analytical solutions and insights clearly, connecting technical findings to business needs.
  • Guide junior team members and strengthen data science practices through continuous improvement, knowledge sharing, and exploration of new methods.
  • Partner across engineering, product, business, finance, and analytics teams to define problems, implement solutions, and communicate results.
  • Establish effective model validation and monitoring practices and use model outcomes to guide ongoing improvements.

Benefits

  • Medical insurance
  • Dental insurance
  • Vision insurance
  • 401(k) matching
  • Generous time off
  • Wellness programs and resources
  • Annual performance bonus eligibility
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