Data Scientist Location: Sherbrooke / Boston / Montréal / Ottawa Employment Type: Full-Time in a fast-growing startup Key Responsibilities • Vector magnetic compensation algorithm development - Design, implement, and validate vector magnetic compensation algorithms; including Tolles-Lawson and extended models to characterise and remove platform-induced magnetic interference across all three field components. Adopt a rigorous testing against ground truth and in-flight datasets, and an awareness of how compensation quality directly impacts end-user navigation performance. • Pattern recognition & signal analysis - Interrogate large, multi-channel magnetic and inertial datasets to identify systematic patterns, interference signatures, and anomalous behaviour; applying statistical analysis, spectral methods, and machine learning techniques to extract actionable insight from complex, noisy signals in operational navigation contexts. • Algorithm testing, benchmarking & iteration Build and maintain structured test frameworks to benchmark compensation performance across platforms, flight regimes, and environmental conditions; tracking residual error metrics, iterating on model parameters, and documenting improvement cycles with reproducible results that can be clearly communicated to navigation system integrators. • Data pipeline development & management - Develop robust, well-documented Python pipelines for ingesting, synchronising, and pre-processing multi-sensor data streams - ensuring consistent data formats, calibration traceability, and version control across field campaigns and laboratory experiments, with outputs structured to meet the ingestion requirements of downstream navigation systems. • Cross-disciplinary collaboration & end-user engagement - Work closely with geophysicists, INS/navigation engineers, and platform specialists to align compensation outputs with navigation requirements; engaging directly with end users in the navigation space to understand operational constraints, gather feedback on delivered data products, and ensure algorithm development remains grounded in real-world mission needs. • Critical evaluation of models & assumptions - Critically assess the validity of compensation models and their underlying assumptions across varying operational contexts; challenging results that appear too clean, identifying failure modes under edge-case conditions, and proposing alternative modelling approaches where standard methods reach their limits, with findings fed back to relevant stakeholders and end users. • Insight communication & technical reporting - Communicate findings clearly to both technical and non-technical stakeholders; including prospective and active end users in the navigation domain. Produce well-structured reports, visualisations, and presentations that distill complex compensation performance results into clear conclusions informing system design decisions, procurement discussions, and operational planning. What We're Looking For • 5 years of experience in a startup environment • Background in magnetic compensation algorithms, ML processing pipelines and data science • Experience in autonomous platforms deployment • Drive to engage with prospective, current clients and engage at conferences to disseminate product knowledge • Comfortable wearing multiple hats and switching contexts quickly • Strong problem-solver with a bias toward action • Excellent communication skills (written and verbal) • Experience with custom Python code Nice-to-Haves • Experience in deeptech, hardware, or scientific environments • Bilingual English/French What We Offer • Flexible hybrid work environment • Opportunity to shape both the company and its culture • Equity • Growth opportunities as SBQuantum scales