Data Scientist (Sherbrooke)

Data Scientist Location: Sherbrooke / Boston / Montréal / Ottawa

Employment Type: Full-Time in a fast-growing startup

About Us SBQuantum is a seed-stage startup at the forefront of magnetic sensing innovation. Our vision is to unlock the full potential of magnetic intelligence through our proprietary quantum diamond technology and curated algorithms.

By combining diamond-based quantum sensors with advanced AI-driven software, we transform complex magnetic field data into actionable, high-value insights. Our solutions enable accurate magnetic mapping, navigation and object detection in environments where traditional sensing technologies—like GPS, radar, imagery, or sonar—cannot perform.

From public safety and defence to space exploration, our multidisciplinary team of engineers, physicists, and data scientists is redefining how the world perceives magnetic signals—turning invisible complexity into clear, useful intelligence.

Why Join Us We’re a fast-growing deeptech startup building multiple bricks for the future of magnetic navigation. This is a unique opportunity to:

Pioneer magnetic navigation for real world applications

Bring your expertise in geophysics to collaborate with industry, government and contribute to an emerging product

Deploy and test your solution on different platforms

Have real ownership and impact in a high-growth, mission-driven environment

Who We’re Looking For We’re looking for a proactive and hands‑on person who thrives in building structure from the ground up and autonomous in making stuff happen. You bring a pragmatic, solution-oriented approach, with the autonomy and drive to move initiatives forward in a fast-paced environment. You’re highly collaborative, able to work seamlessly across technical and non-technical teams and motivated by delivering meaningful impact through strong execution.

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 rigorous testing against ground truth and in‑flight datasets, and be aware 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; track residual error metrics, iterate on model parameters, and document 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; engage 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; challenge results that appear too clean, identify failure modes under edge‑case conditions, and propose 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; include prospective and active end users in the navigation domain. Produce well‑structured reports, visualisations, and presentations that distil 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

Chance to shape both the company and its culture

Equity

Growth opportunities as SBQuantum scales

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