Overview

Responsibilities

• Explore and characterize high-dimensional physical measurement data, including quality, variability, outliers, and uncertainty.
• Build strong baselines and compare statistical, supervised, semi-supervised, and unsupervised approaches.
• Improve anomaly detection, outlier detection, clustering, and classification models with attention to explainability and repeatability.
• Diagnose data issues and model behavior in the existing production system, including changing distributions and unexpected edge cases.
• Develop useful features and representations with hardware, RF, software, and domain specialists.
• Design evaluation methods for limited labels, imbalanced classes, shifting operating conditions, and false-positive trade-offs.
• Read and make targeted, tested contributions to the object-oriented Python ML framework.
• Partner with software and MLOps engineers on integration, reproducibility, packaging, deployment, monitoring, and traceability.
• Document experiments, assumptions, results, and limitations clearly enough for others to review and reuse the work environments.

Required Qualifications

• Typically, 2+ years of applied data science or machine learning experience, or equivalent substantial applied research experience.
• Strong foundations in statistics, classical machine learning, experimental reasoning, and model validation.
• Hands-on experience with anomaly detection, outlier or novelty detection, clustering, classification, or closely related methods.
• Proficiency with Python and common scientific-computing tools such as NumPy, pandas, SciPy, and scikit-learn.
• Ability to navigate modular production code, contribute testable changes, and participate constructively in code review.
• Sound judgment about leakage, class imbalance, threshold selection, uncertainty, and performance trade-offs.
• Clear communication and the ability to work across data science, engineering, and hardware disciplines

Nice to Have

• Sensor, time-series, spectral, RF, industrial, or scientific measurement data.
• Signal processing, experimental design, weak supervision, or learning with sparse labels.
• Distribution shift, experiment tracking, model monitoring, or Azure-based ML workflows.
• Manufacturing, test and measurement, quality assurance, predictive maintenance, or cyber-physical systems.

Export Control, Controlled Goods & Security Eligibility

• This position requires direct or indirect access to hardware, software, or technical information controlled under the Canadian Export Control List, the Canadian Controlled Goods Program, the Canadian Industrial Security Program, the U.S. International Traffic in Arms Regulations (ITAR), and/or the U.S. Export Administration Regulations (EAR).
• All applicants must be eligible or able to obtain authorization for such access, including eligibility for the Canadian Controlled Goods Program, and must be able to obtain a Canadian NATO Secret clearance.

Why Join Palitronica?

• Work with cutting-edge technology at the intersection of cybersecurity, AI, and advanced electronics testing.
• Help protect critical supply chains and infrastructure.
• Join a high-growth company serving some of the world's most demanding industries.
• Collaborate with a talented and mission-driven team.
• Make a measurable impact on the company's growth trajectory.
• Palitronica is an equal opportunity employer and welcomes applications from all qualified candidates.

Use of AI in Hiring