AI/ML Data Scientist

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January 28

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COMERIT

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Artificial Intelligence • SaaS • Enterprise

COMERIT is a leading data transformation and advanced analytics consulting company that empowers businesses to unlock the potential of their ERP data through AI-driven insights and automated workflows. With a focus on streamlining business processes and enhancing decision-making, COMERIT collaborates closely with clients to deliver tailored solutions that improve efficiency and reduce errors. Their expertise spans various technologies and platforms, enabling them to offer innovative Cloud and data enablement services that drive significant value and ROI for organizations.

51 - 200 employees

Founded 2007

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

📋 Description

• This is a remote position. • Comerit is looking for an experienced and driven Google AI/ML Data Scientist to join our team and play a key role in the Border Wait Time (BWT) project. • In this position, you will prepare and process complex traffic congestion datasets, develop machine learning models to predict traffic volumes, and deploy scalable solutions using Google Vertex AI. • Your work will help improve real-time decision-making, enhance border efficiency, and optimize travel experiences for millions of users.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. • 3+ years of experience in machine learning, including data preprocessing, feature engineering, and model development. • Proficiency in Python and ML libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn. • Hands-on experience with Google Cloud Platform (GCP), particularly Vertex AI, BigQuery, and Cloud Storage. • Strong understanding of statistical modeling, time series analysis, and predictive analytics. • Familiarity with version control tools (e.g., Git) and collaborative coding practices. • Experience working with traffic or congestion datasets. • Expertise in real-time data integration and analytics. • Knowledge of containerization tools (e.g., Docker, Kubernetes) for deploying ML models. • Understanding of data privacy and compliance considerations in cloud-based ML projects.