Data Scientist

We are seeking a highly skilled data scientist with operational experience in developing and deploying gradient-boosting algorithms. Reporting directly to the Vice President of Data Science and AI, the successful candidate will play a key role in our data science initiatives and work closely with a dedicated team of machine learning engineers. The ideal candidate should have a strong foundation in probability/statistics, gradient boosting algorithms, and ML Ops. This role involves developing and implementing machine learning models, managing the deployment process, ensuring the models' scalability and security, and accommodating model drift.

As a Data Scientist, you will be at the forefront of extracting valuable insights from various online and offline data signals. You will drive innovation by developing a machine learning interface that leverages the power of foundation models to solve a myriad of tasks in predictive analytics – segmentation, forecasting, and classification. This is a unique opportunity to be at the forefront of building the next generation of enterprise-level machine learning systems.

You will use cutting-edge data science techniques to identify trends, interpret complex data, and provide data-driven recommendations that directly impact key business decisions. We foster a culture of innovation and expect you to play an integral part in advancing our product suite, spanning from data development to software-as-a-service (SaaS) tools. You will be encouraged to challenge the status quo, introduce new tools and techniques, and bring forward fresh ideas that drive our mission forward.

Responsibilities and Duties:

• Build and deploy machine learning models to solve complex business problems.
• Apply advanced statistical and predictive modeling techniques to build, maintain, and improve on multiple real-time decision systems.
• Use gradient boosting frameworks such as XGBoost, LightGBM, CatBoost, and related technology to build and train models.
• Collaborate with other Data Scientists as well as Data and ML Engineers to develop data and model pipelines.
• Assist in deploying models into production and monitor their performance with high governance standards.
• Identify and recommend new applications of machine learning across the organization.
• Conduct research and analysis to identify novel machine learning applications across the organization.
• Make recommendations on ML best practices, tools, and standards.
• Collaborate with the Product organization to develop novel products and solutions.
• Improve business processes through data-driven insights.
• Provide insights and suggestions for process improvements.
• Understand business needs to guide the development of effective data models.
• Adhere to ethical principles in machine learning.
• Understand and adhere to ethical implications of model development and data usage, ensuring fairness and avoiding bias.

• Strong foundation of statistics, probability, and mathematics.
• Demonstrated proficiency with at least one of the following: XGBoost, LightGBM, CatBoost, SageMaker Pipelines (or other cloud providers for ML development and orchestration).
• Demonstrated experience with hyperparameter tuning libraries including Optuna, Hyperopt, or other autoML methods.
• Demonstrated experience with Machine Learning Ops, including deployment and management of models in production.
• A scientific mindset: The ability to solve problems through a combination of deep research, experimental design, hypothesis testing, and analysis.
• Exceptional communication and collaboration skills