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Vetted SciPy Professionals
Pre-screened and vetted.
Junior Software Engineer specializing in autonomy, perception, and aerospace/defense systems
Mid-level AI Engineer and Data Analyst specializing in ML, NLP, and financial forecasting
Senior Full-Stack Engineer specializing in Python backends and modern web apps
Mid-level Data Scientist specializing in AI/ML for financial services and retail analytics
Senior Backend Engineer specializing in Python APIs, cloud platforms, and real-time data systems
Mid-level Data Analyst specializing in predictive analytics and BI for financial services
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Junior Research Data Scientist specializing in healthcare analytics and real-world evidence
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Mid-level Data Engineer specializing in cloud data platforms and FinTech analytics
Senior Full-Stack Developer specializing in cloud-native microservices (AWS)
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Nightvid Cole
Senior Computer Vision & Sensor Algorithms Engineer specializing in imaging systems
“Robotics/remote-sensing software engineer who built and validated multisensor image-processing and spectral chemical-detection pipelines (RX anomaly detection, ACE), including calibration protocols with a motorized shutter and rigorous data QC. Uses white-box NumPy simulators to debug SLAM/registration issues before translating logic to C++, and partnered with hardware teams to solve temperature-driven signal variation via combined software calibration and improved thermal management.”
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Dylan Zhu
Mid-level Machine Learning Engineer specializing in computer vision and generative AI
“Built and deployed an LLM/RAG system that uses differential privacy and distributional similarity checks to transform private data into a non-sensitive knowledge base while preserving utility. Also has experience demonstrating adversarial ML concepts (FGSM) to non-technical audiences by focusing on observable model behavior rather than implementation details.”