Prognostics Research Engineer

Introduction

Are you passionate about leveraging modern-day data science methodologies and tools to study and predict the degradation or occurrence of a problem in a vehicle component or system? Would you love to accelerate efforts to build amazing experiences and software products in the Connected Vehicles space with data? We are seeking top-tier Applied Data Science professionals who are data-driven, self-motivated, and detail-oriented to help develop and deliver breakthrough Prognostic Features.

Required Skills & Qualifications

• Master’s in Mechanical, Electrical, Computer Science, Computer Engineering, Physics, Mathematics, or related fields or a combination of education and equivalent experience
• 4 years of experience practicing statistical methods and their accurate application, e.g., ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multivariate analysis, Neural Networks, causal inference, Gaussian regression, etc.
• Experience with Python (and related modules), SQL
• Experience with embedded controls, onboard diagnostics, sensor processing, general first principles physics modeling, and simulation using numerical computational tools (e.g., MATLAB, ATI, Simulink)
• Experience with Digital Signal Processing (DSP) data structures, algorithms, and software engineering principles
• Self-motivated, strong analytical, excellent interpersonal, and communication skills required
• Prior work experience at client or in client's Industry
• Applicants must be able to work directly for Artech on W2

Preferred Skills & Qualifications

• PhD in Mechanical, Electrical, Computer Science, Computer Engineering, Physics, Mathematics, or related fields or a combination of education and equivalent experience
• Experience in Dynamic Systems, Control, Robotics, Prognostics and Health Management
• Familiarity working with Automotive prognostics feature development using connected vehicle data
• Experience in the application of statistical and machine learning methods e.g., ANOVA, PCA, clustering methods, causal inference, time series forecasting, random forest, multivariate analysis, neural networks, etc.
• Expertise in open-source data science technologies such as Python, R, Spark, Hadoop, etc., acquired through college coursework, online training and certification, or project development
• Experience in software development for automotive controls with hands-on experience using MATLAB for large-scale data and understanding of programming fundamentals and experience with C programming in embedded environments
• ATI and ETAS calibration tool familiarity
• Excellent verbal and written skills
• Highly credible in organizational, time management, decision making, and problem-solving skills

Day-to-Day Responsibilities

• Own the process for prognostic feature development from conceptual to feature deployment to production vehicles
• Pioneer Physics-Informed Machine Learning (PIML) by fusing first-principles physics modeling with advanced machine learning
• Architect and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems
• Translate complex predictive models into highly optimized, low-latency C code for deployment on vehicle electronic control units (ECUs)
• Architect custom Digital Signal Processing (DSP) pipelines and time-series analytics for high-frequency signal processing
• Develop and validate multi-sensor anomaly detection frameworks for real-time Fault Detection and Isolation (FDI)
• Leverage advanced statistical methods to differentiate between correlation and true physical root causes of component degradation
• Direct the entire prognostic lifecycle from mathematical conceptualization to production vehicle deployment
• Partner with component subject matter experts to translate domain knowledge into diagnostics
• Ingest and process large-scale telemetry data using Python, SQL, Spark, and Hadoop

For immediate consideration please click APPLY to begin the screening process with Alex.