Principal Data Scientist

Job Overview

The Principal Data Scientist will spearhead the creation and implementation of sophisticated machine learning models aimed at enhancing personalized customer decision-making. This position blends expert technical skills with leadership responsibilities to build scalable analytics solutions, efficient data pipelines, and deliver valuable insights throughout the enterprise.

Key Responsibilities

• Design and deploy advanced propensity models focused on cross-selling and upselling strategies.
• Develop and optimize Next Best Action and Next Best Offer recommendation engines.
• Apply expertise in supervised learning techniques, uplift modeling, and causal inference methods.
• Implement robust and maintainable codebases primarily using Python; knowledge of R or Scala is beneficial.
• Leverage distributed computing technologies such as Spark, Dask, or Ray for large-scale data processing.
• Build and maintain production-grade machine learning pipelines with tools like Airflow, MLflow, or Kubeflow.
• Ensure adherence to software development best practices including version control, continuous integration and delivery, and testing.
• Manage data workflows on cloud platforms including AWS, GCP, or Azure, and utilize data warehouses such as Snowflake, BigQuery, or Redshift.
• Write and optimize complex SQL queries to handle extensive datasets efficiently.
• Deploy machine learning models into production environments via APIs or streaming platforms like Kafka or Flink.
• Use tools for model version control, experiment tracking, and deployment such as MLflow, SageMaker, or Vertex AI.
• Establish monitoring systems to detect model drift and performance degradation using Prometheus, Grafana, Evidently, or custom dashboards.
• Maintain logging and performance profiling systems for ML services.
• Integrate Generative AI techniques including working with large language models, embeddings, prompt engineering, and vector databases like FAISS or Pinecone into decisioning frameworks or customer-facing solutions.
• Lead a team of senior data scientists while continuing to contribute technically.
• Collaborate effectively with product managers, engineers, and business stakeholders to align analytic solutions with organizational goals.

Qualifications

• Possess a bachelor's degree in fields such as Econometrics, Actuarial Science, Mathematics/Statistics, Computer Science or related disciplines.
• At least 10 years of experience serving in senior data scientist roles, demonstrating leadership and technical prowess.
• Proven modeling expertise with knowledge of cloud platforms and Generative AI technologies.
• Analytics experience within the banking sector is preferred and considered an advantage.

Benefits

• Competitive, performance-based compensation reflecting a pay-for-performance culture.
• An inclusive and diverse working environment.
• Structured opportunities for career advancement and professional growth.
• Work alongside highly skilled professionals in the field.
• Supportive organizational culture encouraging employees to bring their authentic selves and excel.