Staff Data Scientist (Canada, EST, Remote)

Mindbridge Analytics

3d•Remote

About The Position

We are looking for a Staff Data Scientist to lead the development of advanced machine-learning capabilities across large-scale enterprise datasets. This is a senior individual-contributor role combining hands-on modelling with technical leadership. You will work on complex problems involving structured, temporal and high-dimensional data, applying modern deep-learning and representation-learning techniques to develop reusable predictive capabilities. You will partner closely with Data Science, Engineering, Product and domain experts to take ideas from research and experimentation through to production. You will also play a key role in shaping technical direction, establishing modelling and evaluation standards, and mentoring other members of the team. The ideal candidate combines strong applied machine-learning experience with the ability to work effectively in ambiguous problem spaces, design rigorous experiments, and translate emerging techniques into practical product capabilities.

Requirements

Substantial experience building and deploying sophisticated machine-learning systems, ideally in environments involving large-scale or complex data.
Strong practical experience in several of the following areas: Deep learning and modern neural-network architectures.
Transformer architectures, attention mechanisms or sequence models.
Representation learning, embeddings or self-supervised learning.
Modelling structured, tabular, temporal, transactional or event-based data.
Developing and evaluating predictive or generative machine-learning models.
Designing controlled experiments and performing rigorous model evaluation.
Working with large, noisy and heterogeneous datasets.
Python and modern machine-learning frameworks such as PyTorch.
Experience with CUDA and RAPIDS.
Production machine learning, including collaboration with ML or data engineering teams.
Mentoring other data scientists or providing technical leadership across complex projects.
PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics or another quantitative discipline, or equivalent practical experience.
Typically 7+ years of relevant industry experience in Data Science, Machine Learning or Applied Research, with demonstrated impact at a senior or staff level.
Strong understanding of machine-learning fundamentals, statistics and experimental design.
Demonstrated ability to independently lead technically complex projects from problem definition through experimentation and delivery.
Strong programming and data-analysis skills.
Ability to reason clearly about ambiguous problems and make pragmatic technical decisions.
Strong written and verbal communication skills, including the ability to explain complex concepts to different audiences.
Track record of collaborating effectively across Data Science, Engineering, Product and business teams.
Evidence of technical leadership through mentoring, setting standards, influencing architecture or defining modelling strategy.
Fulfill requirements necessary to obtain full background check.

Nice To Haves

Experience in financial services, accounting, payments, ERP systems or other enterprise data domains would be advantageous but is not required.
Experience developing foundation models, recommender systems, anomaly-detection systems, time-series models or other large-scale representation-learning systems would also be valuable.

Responsibilities

Lead the design, development and evaluation of advanced machine-learning models for large-scale structured and transactional data.
Explore and apply modern techniques including transformers, sequence modelling, self-supervised learning and representation learning.
Design robust experiments, benchmarks and evaluation frameworks to compare modelling approaches and measure generalization.
Analyze complex datasets to identify data-quality issues, behavioural patterns, modelling opportunities and potential sources of bias or leakage.
Develop reusable representations and modelling approaches that can support multiple downstream use cases.
Work closely with engineering teams to ensure models can be trained, deployed and operated reliably at scale.
Partner with Product and domain experts to identify high-value applications and translate technical advances into customer-facing capabilities.
Set high standards for modelling quality, reproducibility, documentation and experimentation.
Provide technical mentorship and guidance to data scientists and machine-learning engineers.
Communicate technical decisions, findings and trade-offs clearly to both technical and non-technical stakeholders.
Contribute to the longer-term machine-learning strategy and technical roadmap.