Data Scientist

Urban SDK

32d•Remote

About The Position

We are hiring a skilled Data Engineer to design, build, and maintain scalable data pipelines and platforms that support our geospatial traffic analytics applications. The ideal candidate will have experience with Python, Databricks, S3, and modern data engineering practices, including automated testing, CI/CD, and data quality monitoring.

Requirements

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.

Nice To Haves

Experience with real-time streaming data and event-driven architectures.
Familiarity with traffic simulation models, urban mobility data, or Intelligent Transportation Systems (ITS).
Proven track record of deploying ML models into production environments.

Responsibilities

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.