Lead Data Engineer

Responsibilities:

• Work cross-functionally with product managers, data scientists, and product engineers, and communicate results to peers and leaders.
• Develop scalable algorithms and methods to provide real-time recommendations.
• Explore new technology shifts to determine how they might connect with the customer benefits we wish to deliver.
• Work with engineering teams to implement new models while considering functionality for recommendation output.
• Collaborate with data scientists to prototype new algorithms and design experiments for evaluation to enhance our marketing efficiency.
• Contribute to a culture of continuous improvement and data-driven results.
• Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
• Run regular A/B tests, gather data, perform statistical analysis, and draw conclusions on the impact of your models.
• Discover data sources and ingest and cleanse them in a meaningful way for data processing.
• Participate in design discussions about new features and approaches to implementing new services.
• Take end-to-end ownership of machine learning systems, from data pipelines and training to real-time prediction engines.
• Partner with data scientists to understand, implement, refine, and design machine learning and other algorithms.

Requirements:

• Bachelor's degree or higher (completed and verified before start) from an accredited university.
• Preferably working experience in the FinTech or credit card industry.
• Knowledgeable with data science tools and frameworks (i. e., Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, and Spark).
• Software engineering fundamentals: version control systems (i. e., Git, Github) and workflows, and ability to write production-ready code.
• Should have a clear understanding of generative AI and various LLM models.
• Databricks Delta Lake architecture and MLflow are added advantages.
• Good knowledge of Azure cloud infrastructure.
• Strong knowledge of machine learning techniques (i. e., classification, regression, and clustering).
• BS, MS, or PhD degree in computer science or a related field, or equivalent practical experience.
• Understand machine learning principles (training, validation, etc. ).
• Expertise in visualizing and manipulating big datasets.
• Knowledge of data query and data processing tools (i. e., SQL).