Staff Data Scientist - MindBridge

Who Is MindBridge & How Are They Changing the World 

MindBridge is an AI-native financial technology company helping finance and audit teams understand risk across complex financial data. Its platform analyzes 100% of transactions at scale to identify risk, explain what is driving it, and help organizations move from detection to investigation and governed action. As AI and automation become more embedded in financial workflows, MindBridge is building the oversight layer that helps organizations maintain financial integrity, accountability and trust.

How Will I Make An Impact? 

• Lead the design, development and evaluation of advanced machine-learning models across large-scale structured and transactional datasets.
• Work at the research frontier of transformers, sequence modelling, self-supervised learning and representation learning, applying these techniques to financial data rather than traditional language use cases.
• Help MindBridge develop reusable representations and modelling approaches that can support multiple downstream product applications.
• Design rigorous experiments, benchmarks and evaluation frameworks to understand model performance and generalization.
• Work with large, noisy and heterogeneous datasets to identify data-quality issues, modelling opportunities, bias and potential leakage.
• Partner closely with Data Science, Engineering, Product and financial-domain experts to move ideas from research and experimentation into production.
• Help ensure models can be trained, deployed and operated reliably at scale on GPU infrastructure.
• Raise the technical bar across the Data Science team through mentorship, modelling standards, reproducibility and strong experimentation practices.
• Contribute to MindBridge's longer-term machine-learning strategy and technical roadmap.

How Do I Know If This Is For Me? 

• You've spent meaningful time building sophisticated machine-learning systems.
• You have hands-on experience with modern neural-network architectures and have worked deeply with transformer architectures, attention mechanisms or sequence models.
• You've trained models at meaningful scale and understand the practical challenges that emerge when training across GPU infrastructure.
• You're highly proficient in Python and PyTorch, withl CUDA and RAPIDS experience.
• You have experience with representation learning, embeddings, self-supervised learning or related techniques.
• You're comfortable designing controlled experiments and rigorous model-evaluation frameworks rather than relying on surface-level model metrics.
• You've worked with large, complex datasets (ideally structured, tabular, temporal, transactional or event-based data).
• You enjoy ambiguous research problems where the answer isn't already known.
• You can independently lead technically complex work while remaining deeply hands-on.
• You're comfortable challenging technical assumptions, creating clarity and helping other strong data scientists level up.

Our Ideal Candidate Looks Like: 

• Approximately 7+ years of relevant Data Science, Machine Learning or Applied Research experience, or equivalent depth gained through research/academia.
• Has deep transformer/model-training experience. Building agents, wrapping frontier LLMs or performing lightweight fine-tuning alone won't provide the depth needed for this particular role.
• Has experience training sophisticated models using PyTorch and GPU infrastructure, including CUDA and RAPIDS.
• Has taken advanced ML work beyond experimentation toward production, or has conducted research-grade model training on meaningful compute infrastructure.
• May come from industry or academia/research. SaaS experience is not required if the technical depth is there.
• Has demonstrated technical leadership through mentorship, setting standards, influencing architecture or defining modelling strategy.
• Holds a PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics or another quantitative discipline, or equivalent practical experience.
• Communicates complex technical ideas clearly and can work effectively with Data Science, Engineering, Product and business/domain stakeholders.
• Financial services, accounting, payments or ERP experience is advantageous but not required.

We understand, accept, and value the differences between people of different backgrounds, genders, sexual orientations, ages, beliefs, and abilities. We are happy to make any accommodations you may need throughout the interview process. We’re committed to creating an inclusive environment and welcome all qualified applicants.

Vacancy: This role is a newly created position

Salary: The expected salary range is $185K-200K CAD + bonus and equity

The Process:

• Initial screening with Artemis Canada
• 45 min interview with CTO and Director of Data Science 
• Technical Interview (conversational, not a live exercise)
• HR interview

And of course, your Artemis Canada consultant, Tara,  will work closely with you throughout every step of the process.

We’d love to hear from you - even if you don’t meet 100% of the requirements! 

Send a note to tara@artemiscanada.com if you or someone you know is interested!