Principal Engineer - Data Science

The Commercial Data & Analytics is part of the Verizon Global Services (VGS) organization. The team supports initiatives to uncover actionable insights that drive business value. As part of this team, you will lead a team of data scientists in uncovering actionable insights that drive significant business value and will leverage your deep understanding of data structures, organization, transformation, and aggregation techniques to lead complex data analysis projects. You will apply your expertise in statistical modeling, machine learning, and advanced analytics to extract meaningful patterns and trends from large and complex datasets. You will guide the team in utilizing their proficiency in Python/R and data visualization tools to effectively communicate findings to stakeholders and influence strategic decision-making at all levels of the organization.

• Lead complex data science projects end-to-end, from ideation and problem definition to model development, deployment, and ongoing monitoring.
• Serve as a thought leader and mentor to other data scientists, providing technical guidance and fostering a culture of innovation and excellence within the team.
• Proactively identify and explore new opportunities to leverage data science and machine learning to solve critical business challenges and drive strategic advantage
• Develop and maintain strong relationships with key stakeholders across the organization, effectively communicating complex technical concepts and insights to influence decision-making at all levels.
• Stay ahead of the curve in data science and AI. Share your expertise and actively explore new technologies to benefit the team.
• Deeply understand business requirements and translate them into well-defined analytical problems, identifying the most appropriate statistical techniques and modeling approaches to deliver impactful solutions.
• Expertly manipulate and prepare data for modeling, leveraging your knowledge of data structures, transformation techniques, and feature engineering to optimize model performance.
• Lead the design, development, and validation of sophisticated statistical models and machine learning algorithms to address complex business challenges and drive strategic decision-making.
• Develop and implement rigorous model validation frameworks to ensure the accuracy, reliability, and generalisability of your models, adhering to best practices in statistical modeling and machine learning.
• Clearly and effectively communicate complex statistical concepts and model results to both technical and non-technical audiences, translating your findings into actionable insights for stakeholders.

You will need to Have:

• Bachelors degree or four or more years of work experience.
• Six or more years of progressive experience as a Data Scientist, with a proven track record of leading and delivering high-impact data science projects
• Deep expertise in a wide range of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, time series analysis, and reinforcement learning.
• Expert-level proficiency in Python and/or R, including extensive experience with relevant machine learning and data manipulation libraries (e.g., scikit-learn, TensorFlow, PyTorch, pandas, NumPy, ggplot2, caret, etc).
• Mastery of SQL and experience with large datasets and distributed computing environments.
• Exceptional communication and presentation skills, with the ability to effectively communicate complex technical concepts and insights to both technical and non-technical audiences, including senior leadership.
• Ability to collaborate effectively across teams for data discovery and validation
• Excellent communication and presentation skills, with the ability to clearly articulate complex technical concepts to both technical and non-technical audiences.

Even better if you have one or more of the following:

• Experience with supply chain and business operations processes
• Experience in maintaining repositories in Git
• Experience with dashboards creation using Tableau/QLIK/Looker
• Expertise in advanced statistical modeling techniques, such as Bayesian inference or causal inference.
• Practical experience or strong theoretical understanding of large language models (LLMs) and their applications in a business context.
• An understanding of the ethical considerations and potential biases associated with generative AI, and strategies for responsible development and deployment.
• Contributions to open-source projects or publications in relevant data science or machine learning conferences.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and deploying machine learning models at scale using platforms like Domino Data Lab or Vertex AI