QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related fields or equivalent hands-on experience. MA in the above fields preferred.
• 1-2 years of experience in a data science role, showcasing solid abilities in SQL and Python.
• Proficiency in utilizing a range of Machine/Deep Learning algorithms and frameworks, including TensorFlow, PyTorch, scikit-learn, Spark ML, Torch, Huggingface, Keras, Caffe, and CNTK.
• Experience of one or more AI/ML platforms in cloud such as Sagemaker, Dataiku, DataRobot, H2O.ai, Snowpark, ModelOp Center, and Domino Data Lab.
• Experience in or understanding of model development, with a willingness to explore model deployment strategies.
• Excellent data analysis, profiling and statistics skills coupled with proficiency in SQL tools and technologies such as Oracle, SQL Server, MySQL, Pandas, NumPy, Ggplot, Shiny, SciPy, Sci-Kit Learn, and Matplotlib.
• High analytical aptitude for navigating complex datasets to extract actionable insights.
• Excellent problem-solving skills and a detail-oriented mindset.
• Strong communication abilities, capable of conveying technical concepts to diverse audiences.
• Proactive in learning and adapting to new methodologies and challenges.
• Ability to work effectively in a team and manage simultaneous projects in a dynamic environment.

CULTURE:

We are seeking enthusiastic candidates who have an unwavering commitment to remain at the forefront of data technology and science. Our ideal candidates are those who aim to foster a team spirit and collaboration and want to learn and grow with our team.

WHAT ELSE YOU SHOULD KNOW:

• We’re a publicly-traded company (TREE).
• We’ve welcomed several other companies into the LendingTree family to augment our efforts at helping borrowers make their most sensible financial choices.
• We’ve built a LendingTree app and LendingTree dashboard to give consumers tools to manage and monitor their financial health.

KEY RESPONSIBILITIES:

• Collaborate with multiple teams to pinpoint opportunities for data analysis that support business goals.
• Handle data collection, cleaning, and preprocessing to ready datasets for comprehensive analysis.
• Ensure the validity, reliability, and robustness of models by implementing best practices in model validation, testing, and calibration.
• Conduct thorough exploratory data analysis (EDA) to uncover insights and inform model development.
• Expertly utilize SQL for data extraction and Python for analysis and modeling tasks.
• Aid in maintaining/retraining existing models.
• Develop predictive models with Python using libraries such as Scikit-learn or PyTorch, focusing on generating insights that directly influence business decisions.
• Engage in projects that may require understanding or executing basic model deployment processes, ensuring insights are accessible to all stakeholders.
• Communicate complex analytic solutions and insights in a clear and structured manner to business stakeholders.
• Foster a culture of continuous improvement within our data science practices, staying abreast of industry trends and advancements.
• Demonstrate an eagerness to learn, including aspects related to bringing data science models into operational use.