Cordia Resources by Cherry Bekaert
Location
Remote | United States
Job description
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.
Qualifications: - 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
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