Job Title: Data Analyst – AI & Predictive Modeling for Fleet Operations Location: Remote Job Type: 6 month+ Position Summary: • We are seeking a detail-oriented and forward-thinking Data Analyst to join our Fleet Data Analytics team. This role will focus on developing an AI-driven predictive model aimed at improving data-driven decision-making, optimizing fleet operations, and supporting strategic initiatives. • As fleet data grows in complexity and volume, this position is critical in helping the organization derive timely, accurate, and actionable insights. You will work with large, multi-source datasets and apply advanced analytics techniques to enhance operational efficiency and planning capabilities across the fleet. Key Responsibilities: • Design, build, and maintain predictive analytics models using AI and machine learning techniques to support fleet management and strategic decision-making. • Apply advanced analytics and machine learning techniques to forecast fleet performance, identify operational risks, and support optimization efforts. • Analyze large datasets from multiple platforms, ensuring data integrity, consistency, and completeness. • Collaborate with stakeholders across departments to understand business needs and implement data-driven solutions that enhance fleet performance and efficiency. • Identify patterns, anomalies, and opportunities within fleet data (e.g., utilization, maintenance, fuel consumption, downtime). • Develop dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders. • Continuously monitor and refine models based on real-world performance, feedback, and evolving business goals. • Stay current on trends and best practices in AI, data science, and predictive analytics applicable to fleet operations. Qualifications: • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or a related field. • 3+ years of experience in data analysis, machine learning, or AI model development. • Proficiency in Python, R, SQL, or equivalent tools for data modeling and analysis. • Experience with data visualization tools (e.g., Power BI, Tableau, or similar). • Demonstrated ability to develop and deploy predictive models using regression, time-series forecasting, classification, or clustering. • Experience working with large datasets and cloud-based data platforms (e.g., AWS, Azure, or GCP). • Strong problem-solving and communication skills with the ability to translate analytical insights into strategic recommendations. Preferred Skills: • Knowledge of fleet operations, transportation logistics, or asset lifecycle management. • Experience with real-time analytics or streaming data. • Familiarity with MLOps, automated model deployment, or model monitoring.