Principal Data Scientist [On-Site, Cities Listed In Posting]

Regions Bank•Birmingham, AL

65d•Onsite

About The Position

At Regions, the Principal Data Scientist is a visionary and hands-on practitioner that supports the Data Science team by deepening technical bench strength across Machine Learning (ML), MLOps and GenAI. This position will drive the development of scalable, efficient, and reliable data-science capabilities that elevate the entire team’s productivity and impact. This includes advancing model lifecycle management, establishing best-in-class practices, designing ML pipelines, and guiding the adoption of emerging technologies like Large Language Models (LLMs) and Generative-AI. The candidate should have comprehensive knowledge of end-to-end data science modeling lifecycles and strong track record of successfully delivering complex data science projects.

Requirements

PhD degree in a quantitative or analytical field such as Statistics, Mathematics, Physics, Computer Science, Engineering, or a related discipline and our (4) years of relevant experience Or Master’s degree in a quantitative or analytical field, such as Statistics, Mathematics, Physics, Computer Science, Engineering, or a related discipline and six (6) years of relevant experience Or Bachelor’s degree in a quantitative or analytical field, such as Statistics, Mathematics, Physics, Computer Science, Engineering, or a related discipline and eight (8) years of relevant experience
Eight (8) years of experience in Machine Learning, with experience in GenAI, LLMs and frameworks such as Hugging Face Transformers and LangChain.
Eight (8) years of programming experience in Python, PySpark, SQL and modern ML Libraries (e.g. Scikit-learn, TensorFlow, PyTorch)
Six (6) years of hands-on experience with Big Data tools and platforms such as Hadoop, Spark, Hive, MLFlow or Kafka
Six (6) years of hands-on experience with cloud-based analytics platforms such as Amazon Web Services (AWS) SageMaker, Azure Machine Learning Studio, Google Cloud AI Platform, Snowflake or Databricks

Nice To Haves

Five (5) years of experience in Agile Software Development Lifecycle
Five (5) years of experience influencing, guiding, or providing technical direction to other data scientists or cross-functional team members.
Background in banking and/or other financial services
Experience in RAG and integrating frontier models such as LangChain, llamaIndex, Anthropic, Bedrock, GPT, or Ollama
Experience with Docker/Kubernetes
Hands-on experience with techniques for text parsing, sentiment analysis, and the use of generative models such as Generative Pre-trained Transformer (GPT), Variational Autoencoders (VAE), and Generative Adversarial Networks (GANs)
Ability to continue research and learn new systems as needed
Ability to partner with stakeholders to identify business challenges and design solutions
Ability to research, analyze data, and derive facts
Ability to work under pressure and meet deadlines
Deep understanding of statistical and predictive modeling concepts, machine learning approaches, clustering and classification techniques, or recommendation and optimization algorithms
Experience delivering and scaling models in production
Experience planning, managing, and delivering data science projects, including risk identification and mitigation
Strong verbal, written communication, and organizational skills
Strong work ethic and self-motivation

Responsibilities

Develops and leads execution of the research roadmaps for advanced AI Capabilities such as LLM, GenAI, Retrieval-Augmented Generation (RAG), Agents and Computer Vision etc.
Stays at the cutting edge of ML/AI literature and practices
Periodically evaluates the design of existing modeling and MLOps and identifies opportunities for improvement
Serve as a technical Subject Matter Expert (SME) on modeling techniques, feature engineering, pipeline orchestration, performance optimization and testing
Partners with other data scientists, engineers and product teams to define technical standards, influence solution-design decisions, and raise the bar on solution quality
Champions MLOps excellence across the team, including CI/CD for ML, automated testing, monitoring, model versioning and rollback strategies
Designs and promotes implementation of reusable codes, tools and frameworks for model development, training, deployment, monitoring and governance
Drives complex, high-impact modeling projects from ideation to production, especially where advanced techniques (e.g. NLP, GenAI, LLMs) can drive strong business value
Leverages statistical analysis, machine learning (ML) and deep learning (DL) techniques, collaborating with various lines of businesses to design data products that enhance profitability, mitigate risks, and drive customer engagement across all touchpoints
Leverages cloud-based analytical platforms to build data analytics solutions
Extracts actionable insights from data to support data-driven decision-making processes
Collaborates with risk management and compliance teams to ensure compliance with internal and external regulatory requirements
Fosters a culture of innovation and continuous improvement within the team