Data Scientist (Transportation & Logistics)

Role Overview: As a Data Scientist in the Transportation & Logistics industry, you will work closely with the business team to understand data sources and perform detailed exploratory data analysis (EDA) to ensure data quality and completeness. Your primary responsibility will be to design, implement, and maintain robust data preparation pipelines tailored to manufacturing datasets in order to support Machine Learning (ML) models. Additionally, you will analyze structured and semi-structured datasets to identify trends, anomalies, and opportunities for improvement. Your role will involve conducting ad-hoc analysis using statistical and machine learning algorithms to address specific business requirements. Collaboration is key, as you will be working in a global, cross-functional team environment utilizing agile methodologies. Key Responsibilities: - Collaborate with the business team to understand data sources and perform detailed EDA to assess data quality and completeness. - Design, implement, and maintain data preparation pipelines specific to manufacturing datasets to support ML models. - Analyze structured and semi-structured datasets to identify trends, anomalies, and improvement opportunities. - Conduct ad-hoc analysis using statistical and machine learning algorithms based on business requirements. - Work effectively in a global, cross-functional team using agile methodologies. Qualifications Required: - At least 4 years of experience in Data Science with a strong focus on Machine Learning. - Hands-on experience in building scalable and deploying ML solutions. - Proven expertise in working with datasets within the transportation, logistics, or fleet management domains. - Experience in projects involving KPIs such as route efficiency, fuel usage, delivery time, and fleet utilization is preferred. - Knowledge of building recommendation systems and personalization algorithms using traditional ML models. - Proficiency in Python, SQL, and ML libraries such as scikit-learn, pandas, etc., along with a solid understanding of OOPs concepts. - Experience with cloud platforms, with a preference for Azure. (Note: No additional company details were provided in the job description.),