Data Scientist Job Openings At UPS

Qualifications

• Expertise in Python, SQL. Experienced in using data science-based packages like scikit-learn, numpy, pandas, tensorflow, keras, statsmodels, etc.
• Strong understanding of statistical concepts and methods (like hypothesis testing, descriptive stats, etc.), machine learning techniques for regression, classification, clustering problems, including neural networks and deep learning.
• Proficient in using GCP tools like Vertex AI, BigQuery, GCS, etc. for model development and other activities in the ML lifecycle.
• Strong ownership and collaborative qualities in the relevant domain. Takes initiative to identify and drive opportunities for improvement and process streamline.
• Solid oral and written communication skills, especially around analytical concepts and methods.
• Ability to communicate data through a story framework to convey data-driven results to technical and non-technical audience.
• Master’s Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.

Bonus Qualifications

• NLP, Gen AI, LLM knowledge/experience
• Knowledge of Operations Research methodologies and experience with packages like CPLEX, PULP, etc.
• Knowledge and experience in MLOps principles and tools in GCP.
• Experience working in an Agile environment, understanding of Lean Agile principles.

Responsibilities:

• Become a subject matter expert on UPS business processes and data to help define and solve business needs using data, advanced statistical methods and AI
• Be actively involved in understanding and converting business use cases to technical requirements for modelling.
• Query, analyze and extract insights from large-scale structured and unstructured data from different data sources utilizing different platforms, methods and tools like BigQuery, Google Cloud Storage, etc.
• Understand and apply appropriate methods for cleaning and transforming data, engineering relevant features to be used for modelling.
• Actively drive modelling of business problem into ML/AI models, work closely with the stakeholders for model evaluation and acceptance.
• Work closely with the MLOps team to productionize new models, support enhancements and resolving any issues within existing production AI applications.
• Prepare extensive technical documentation, dashboards and presentations for technical and business stakeholders including leadership teams.