Lead Data Scientist

Praxy; Canada; Hybrid; CAD 124,000 - 165,000; Part time

Benefits offered by this job

CAD $100 per hour

Fully remote in Canada

Ad-hoc hourly engagement

Work on production-grade AI projects

Autonomy and leadership

Job summary

Praxy is seeking a Lead Data Scientist in Canada to own end-to-end data science engagements, from architecture through deployment and monitoring. You will work directly with clients, translate objectives into technical strategies, and mentor engineers on DataOps/MLOps practices.

The role focuses on computer vision, retrieval systems, and production ML across diverse industries, with full remote flexibility for Canada-based professionals.

Qualifications

• Master's degree in a quantitative field; PhD is a strong advantage.
• 5+ years shipping models or data systems used by real users.
• Expert Python engineering with production services and testing.
• Strong AWS (SageMaker, RDS, S3) and IaC (Pulumi/Terraform).
• Experience with LLMs, retrieval/RAG, and data-centric AI.

Responsibilities

• Own data science components end to end from architecture to production deployment.
• Translate client objectives into practical technical strategies and deliverables.
• Design rigorous evaluation strategies and monitoring for deployed systems.
• Mentor engineers and contribute to DataOps/MLOps practices.
• Present results clearly to non-technical clients and stakeholders.

Skills

Python production

Computer vision

Experiment design

Data engineering

AWS SageMaker

Terraform

Pulumi

GitHub Actions

Retrieval systems

LLM application

Education

Master's degree in Data Science/CS

PhD considered strong advantage

Tools

PostgreSQL

Job description

# Lead Data Scientist: Canada: Contract: Sep 14, 2026: Sep 17, 2026## About the roleThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist based in Canada.This is a senior, client-facing data science role focused on turning ambiguous business challenges into reliable, production-ready data and AI systems.You will own the data science workstream from architecture and experimentation through deployment, monitoring, and continuous improvement.The role combines advanced modeling with hands-on data engineering, infrastructure, retrieval systems, and production ML development.You will work directly with clients and senior stakeholders, translating complex technical findings into clear business decisions and measurable outcomes.Projects span applied computer vision, agentic AI, retrieval systems, data platforms, and connected-device solutions across diverse industries.Success requires strong technical judgment, commercial awareness, leadership, and the ability to thrive in complex projects with real deadlines.This is an opportunity to lead sophisticated AI initiatives while mentoring engineers and helping shape best practices across the broader data solutions practice.Accountabilities- Own the data science components of client engagements end to end, from architecture and evaluation design through production deployment and monitoring.- Collaborate with clients, sales, engineers, and designers to define and estimate Statements of Work, including assumptions, risks, dependencies, timelines, and effort.- Translate ambiguous client objectives into practical technical strategies, making independent architecture and implementation decisions within project constraints.- Design rigorous evaluation strategies that reflect real-world system usage, including appropriate metrics, validation approaches, test datasets, and regression testing.- Build and deploy reliable data, retrieval, machine learning, and AI pipelines into production environments.- Develop applied computer vision solutions, including dataset creation, model training, evaluation, transfer learning, inference, and production deployment.- Architect retrieval and RAG systems using lexical and semantic approaches, embeddings, hybrid retrieval, ranking, and LLM-based applications.- Establish correctness, auditability, reproducibility, reconciliation, and traceability so system outputs can be connected back to their underlying data sources.- Engineer robust data pipelines capable of acquiring, normalizing, processing, and reconciling data from unreliable or externally controlled sources.- Present technical results, limitations, risks, and recommendations directly to non-technical clients using clear, business-oriented language.- Challenge ineffective or unsuitable client approaches constructively and propose stronger alternatives aligned with business outcomes.- Identify early when data quality, data volume, architecture, or strategy needs to change rather than relying on additional model tuning.- Scope and estimate work independently, manage delivery against agreed time constraints, and maintain accountability for project outcomes.- Write concise technical documentation, model reports, schema documentation, summaries, and reusable repositories that other engineers can operate and extend.- Mentor senior engineers, review technical work, and identify weaknesses in experimental design, model training, or production implementation before deployment.- Establish and improve DataOps and MLOps practices, contributing to the continued evolution of data and AI delivery capabilities.- Support commercial activities as a solutions engineer, helping define technically sound scopes and delivery approaches that support successful client engagements.Requirements- Master's degree in Data Science, Computer Science, or a related quantitative discipline; a PhD or equivalent research training is considered a strong advantage.- 5+ years of relevant professional experience, including experience shipping a model or data system that real users depend on.- Expert-level Python engineering skills, including production services, packaging, testing, code review, and writing maintainable code that can be handed off to other engineers.- Strong applied computer vision expertise, including modern transformer-based and CNN architectures, multiple task types, transfer learning, fine-tuning, learning-rate scheduling, regularization, and early stopping.- Strong understanding of experimental methodology, including leakage prevention, train/validation/test design, systematic error analysis, appropriate metric selection, and disciplined experiment tracking.- Advanced PostgreSQL expertise, including schema design for complex domains, indexing, query optimization, full-text search, and pgvector.- Proven data engineering experience with unreliable sources, including bulk acquisition, normalization, idempotent and incremental loading, and reconciliation against external sources.- Experience constructing datasets from raw source material, maintaining source traceability, working with labeling platforms, and connecting datasets to imperfect real-world ground truth.- Experience building lexical and semantic retrieval systems, including chunking strategies, embedding selection, hybrid retrieval, and rank fusion.- Experience with LLM application engineering, including MCP or equivalent typed tool interfaces, grounding, source citation, validation, and hallucination mitigation.- Strong AWS experience, including SageMaker, GPU instance selection and cost management, Fargate, RDS, S3, and hosted inference endpoints.- Experience with infrastructure-as-code using Pulumi or Terraform and CI/CD using GitHub Actions.- Demonstrated experience leading cross-functional engineering teams and collaborating with sales and client success teams to secure or expand client engagements.- Excellent written and spoken English communication skills, with the ability to influence technical and non-technical stakeholders.- Strong client orientation, business judgment, and the ability to measure technical decisions by their impact on client outcomes.- Ability to lead through ambiguity and complexity, establish direction under pressure, and maintain high technical and delivery standards.- Strong ownership, resilience, and enthusiasm for solving complex, high-stakes technical problems.- Must reside in Canada and be legally authorized to work as an independent contractor in Canada.- Must have access to a personal computer/device and reliable internet connection.- Willingness to travel up to approximately 10% of the time for client or team needs.- Nice-to-have experience includes Azure services, classical computer vision and object detection, temporal/video/pose estimation, managed authentication and JWT validation, consulting or agency delivery, and AWS Professional-level certification.- 7+ years of relevant experience is also considered an advantage.Benefits- Compensation: CAD $100 per hour for candidates leveled as Lead during the interview process.- Fully remote opportunity for professionals residing in Canada.- Flexible independent-contractor engagement structured on an ad-hoc hourly basis.- Opportunity to work on meaningful, production-grade AI and data projects for North American clients.- Exposure to complex technical challenges spanning computer vision, LLMs, retrieval systems, data engineering, cloud infrastructure, and connected technologies.- High degree of autonomy and ownership over technical architecture, delivery, experimentation, and client outcomes.- Direct exposure to senior client stakeholders and opportunities to influence business and technical strategy.- Collaborative environment with software, hardware, design, engineering, sales, and client-success professionals.- Opportunities to mentor other engineers and contribute to the development of DataOps, MLOps, and broader data engineering practices.- Meaningful professional growth through challenging projects and high technical standards.- Important: This is an independent contractor engagement and does not include employee benefits such as group RRSP matching, extended health coverage, equipment stipends, or education stipends.- Contractors are responsible for providing their own computer/device and reliable internet connection.- Visa sponsorship is not provided.