About Technosylva

Technosylva is a rapidly growing SaaS company and the industry leader in wildfire operational support and risk analytics. Technosylva exists to reduce the impact of wildfires by providing proactive and actionable intelligence. We provide wildfire risk mitigation software solutions for some of the nation’s largest Investor Owned Utility (IOU) companies, wildfire agencies, and other organizations. Technosylva's suite of wildfire risk analysis products are specifically tailored to meet the needs of electric utilities and government agencies, but are quickly finding traction with an expanding marketplace that will include transmission operators, insurance, and others. They enhance operational and mitigation decision-making with proactive risk forecasting, on-demand fire spread predictions, asset hardening analysis, and strengthen regulatory compliance & reporting efforts.

Technosylva has offices in La Jolla, CA, and León, Spain. The company has been providing critical solutions for the past 26 years, however the organization has been on a journey of transformation and rapid growth over the past 3 years, scaling to about ~150 employees and providing its product offerings in over 10+ countries. TA Associates, a leading growth PE firm recently made a significant investment in Technosylva to amplify the company’s mission and continued growth trajectory.

Overview

We are seeking an expert to design, build, and automate predictive analytics systems that forecast weather variables using satellite data and numerical weather prediction (NWP) model outputs as predictors. The ideal candidate will collaborate with atmospheric scientists to develop machine learning (ML) platforms that generate short- and long-term weather forecasts for specific locations, regions, and grids using complex 4D datasets. This position will report directly to the principal scientist.

Responsibilities

• Create machine learning models that predict weather variables, including extreme weather events, using sparse data.
• Choose the most suitable machine learning algorithms based on the specific characteristics and requirements of the dataset.
• Work closely with atmospheric scientists to understand weather patterns and integrate their expertise into the development of accurate forecasts.
• Accelerate and automate the training and testing processes of ML models on GPU-based systems for faster, more efficient performance.
• Handle and analyze complex, multi-dimensional weather data from satellite and numerical weather prediction models.
• Design and implement platforms that generate weather forecasts for specific locations, regions, and grids.
• Contribute to the development of long-term ML design strategies and technology frameworks for the company’s growth.
• Optimize ML models for scalability and performance, particularly when processing large datasets on high-performance computing systems.
• Support the data engineer with various data storage design and implementation projects.
• Assist in the development of future ML programs that support the mission-critical tasks of the company and clients.

Required Skills

• Expertise in statistical models and numerical methods for forecasting weather data and extreme events.
• Ability to create, automate, and interpret diagnostic tools for model validation and performance evaluation.
• Ability to design and implement solutions that predict extreme events, particularly those trained on sparse data.
• Operating Systems: Proficiency with Linux (Ubuntu), including experience optimizing Linux desktops and servers.
• Programming Languages:
  • Python 3.x with libraries such as Numpy, Pandas, Xarray, and Dask for data manipulation and analysis.
• Machine Learning Algorithms & Frameworks:
  • Experience with algorithms such as Self-Organizing Maps, Ensemble Learning (e.g., Random Forest), Support Vector Machines (SVM), and Artificial Neural Networks (ANN).
  • Familiarity with Scikit-learn, XGBoost, and RAPIDS for machine learning model development and optimization.
• Databases: Experience with relational databases such as PostgreSQL/PostGIS and/or MySQL/MariaDB for storing and managing large datasets.
• Container Technologies: Familiarity with Docker for creating and managing containers for ML and data processing tasks.

Preferred Skills

• Programming languages:
  • Python 3.x.
• Hardware:
  • GPU systems, high-performance Computing Clusters.