Data Scientist

Role Overview

This role focuses on spearheading the creation and implementation of advanced analytics and artificial intelligence solutions aimed at improving public transportation operations, boosting asset reliability, optimizing maintenance, and enhancing commuter satisfaction. The position requires acting as both a technical expert and a collaborative partner, transforming data into practical insights that drive measurable operational improvements.

Key Responsibilities

• Oversee the complete lifecycle of machine learning and AI initiatives, including defining business problems, exploring and engineering data, training models, validating outcomes, deploying solutions, and monitoring ongoing performance.
• Design and deploy machine learning, AI, or optimization models to tackle diverse business and operational issues such as performance enhancement, demand forecasting, resource optimization, system reliability, and improved customer experiences.
• Work closely with data and software engineering teams to implement ML/AI models in secure air-gapped and cloud environments.
• Lead experimentation and foster continuous improvement using AI methods including time-series forecasting, anomaly detection, computer vision, and natural language processing.
• Communicate insights and recommendations based on data in clear business language to relevant stakeholders.
• Collaborate with business analysts to engage end-users in identifying and assessing promising AI use cases within the transit context.
• Manage AI project workflows and maintain AI infrastructure capabilities.

Qualifications and Experience

• A Bachelor's or Master's degree in computer science, computer engineering, statistics, data analytics, applied mathematics, or a related discipline.
• Minimum of eight years of pertinent data science experience, with at least two years serving in a lead or senior technical capacity.
• Strong skills in Python programming, workflow tools such as Nifi and Airflow, and SQL.
• Hands-on experience with data manipulation and machine learning libraries including pandas, numpy, scikit-learn, TensorFlow, and PyTorch.
• Familiarity with MLOps platforms such as Databricks, Snowflake, and AWS SageMaker.
• Competence in data visualization tools like Power BI, Qlik, and Tableau for effective presentation of analytic insights.