Lead the end-to-end development of machine learning and statistical models for traffic prediction, route optimization, congestion analysis, and mobility analytics
Design and implement scalable data pipelines and workflows for processing large-scale geospatial and temporal datasets
Analyze traffic patterns using geospatial data and sensor feeds (GPS, IoT, traffic cameras, etc.) to derive actionable insights
Collaborate with engineering teams to integrate machine learning models into production systems
Conduct exploratory data analysis (EDA), feature engineering, and model evaluation to ensure robust and accurate predictions
Stay current with the latest advancements in machine learning, deep learning, geospatial analytics, and big data technologies
Mentor junior data scientists and provide guidance on best practices for model development and data analysis
Present findings and insights to cross-functional teams, stakeholders, and clients in a clear and actionable manner.

Master's or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related field
5+ years of experience in data science, machine learning, or related fields
Strong proficiency in Python and associated libraries (pandas, NumPy, scikit-learn, TensorFlow/PyTorch, etc.)
Hands-on experience with big data platforms and tools (Spark, Hadoop, Hive, or similar)
Solid understanding of geospatial data analysis, GIS tools (e.g., PostGIS, QGIS), and geospatial libraries in Python (e.g., GeoPandas, Shapely)
Experience with predictive modeling, time series analysis, and optimization algorithms
Knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes) is a plus
Strong analytical, problem-solving, and communication skills.

Data Scientist

Key skills