Required Skills
- 10+ years of hands-on experience in Applied Data Science, Analytics Engineering, and Systems Modeling.
- 5+ years of client-facing, consulting, or business development experience delivering analytics solutions.
- Expertise in statistical modeling, machine learning, and predictive analytics.
- Strong proficiency with Python, scikit-learn, statsmodels, PyTorch, and TensorFlow.
- Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques.
- Strong expertise in geospatial analytics and LiDAR data processing.
- Hands-on experience with ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related geospatial libraries.
- Experience working with vector, raster, point-cloud, and sensor datasets.
- Excellent analytical, communication, and stakeholder management skills.
Roles & Responsibilities
- Design and develop advanced machine learning and statistical models to solve complex business problems.
- Build predictive models, time-series forecasting solutions, and causal inference frameworks.
- Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets.
- Develop geospatial analytics and LiDAR processing solutions using industry-standard tools and libraries.
- Analyze vector, raster, point-cloud, and sensor data to generate actionable insights.
- Partner with business stakeholders to scope, design, and deliver data science solutions.
- Present analytical findings and recommendations to technical and business audiences.
- Optimize model performance, scalability, and deployment in production environments.
- Mentor data scientists and promote best practices in analytics and machine learning.
- Support innovation initiatives through advanced analytics and AI-driven solutions.