Description

Architect scalable data pipelines and deploy predictive models to support infrastructure planning, operational decision-making, and regulatory analysis.

This role is remote.

Responsibilities

• Build and maintain end-to-end data pipelines for structured, unstructured, and geospatial data sources.
• Design, validate, and deploy machine learning models, including time-series forecasting and ensemble methods.
• Develop interactive dashboards and visualizations to translate complex model outputs into actionable insights.
• Lead the incubation of new data-centric products, prototyping concepts and defining scalable architectures.
• Establish firm-wide standards for applied data science, model lifecycle management, and responsible AI.

Required Skills

• 10+ years of hands-on experience in applied data science, analytics engineering, and systems modeling.
• Mastery of Python data science ecosystem (pandas, Polars, NumPy, SciPy) and advanced SQL.
• Expert proficiency in statistical modeling and ML frameworks (scikit-learn, statsmodels, PyTorch, TensorFlow).
• Strong expertise in geospatial analytics and LiDAR processing using ArcGIS, PostGIS, GeoPandas, GDAL/OGR, and PDAL.
• Deep MLOps experience: containerization, orchestration, CI/CD, experiment tracking, and deployment on Azure, AWS, or GCP.
• Proficiency in Power BI, Tableau, Plotly/Dash, and geospatial visualization tools.
• Experience with Jira, Smartsheets, and MS Project for project management and delivery.

Preferred Skills

• Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related quantitative field (or Bachelor’s with 10 years experience).
• Minimum 5 years in client-facing, consulting, or business development roles.

Key Skills

Education

Bachelor's or Master's degrees