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Scientific Machine Learning Jobs in Quebec (NOW HIRING)

Develop statistical and machine learning models: regression, forecasting, classification ... Mentor junior data scientists and use AI-assisted tools (Claude Code, Gemini CLI) to raise the ...

Join our Machine Learning Platform team and help bring models, including Large Language Models ... Collaborate with applied scientists, data engineers, and developers to integrate agentic ...

Join our Machine Learning Platform team and help bring models, including Large Language Models ... Collaborate with applied scientists, data engineers, and developers to integrate agentic ...

Develop, validate and deploy statistical, machine learning and generative AI solutions, including ... Lead a small team of data scientists, providing technical leadership, mentorship and quality ...

... Machine Learning Minimum Qualifications * MS/M.Tech/M.Math/ or PhD in Computer Science, Statistics, Mathematics, Physics, Developer, Economics, Computational Linguistics or related fields * 3+ years ...

We are seeking a Senior Machine Learning (ML) Research Scientist to join our team working on a novel AI safety agenda. In this role, you will design and implement innovative ML models aimed at ...

We are seeking a senior distributed machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to ...

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or equivalent practical experience * 8+ years building cloud-native software in production (distributed systems ...

Data science enthusiasts with a background in a related field, including (but not limited to ... Understanding of underlying Machine Learning theory and the Predictive Modeling Lifecycle.

Apply AI and machine learning techniques to develop and evaluate predictive models * Communicate ... Currently pursuing a bachelor's degree or higher in computer science, statistics, applied ...

Showing results 41-60

Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Quebec?

For Scientific Machine Learning jobs in Quebec, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Quebec look for?

The top searched job categories for Scientific Machine Learning jobs in Quebec are:

Infographic showing various Scientific Machine Learning job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution.

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 18 days ago


Key responsibilities

  • Own models end to end, including problem framing, development, deployment, and monitoring.

  • Develop and fine-tune statistical and machine learning models such as regression, forecasting, classification, clustering, and causal inference.

  • Create data pipelines for ingestion, transformation, and quality assurance across diverse data sources.


Job description

About the job

Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain.We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise - we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions.

As Artefact continues to grow in the US, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption.

Who We Are

Founded and headquartered in Paris, Artefact is a next-generation consulting firm, specializing in data, analytics & AI consulting, dedicated to transforming data into business impact across the entire value chain of organizations. We are proud to say that we help our clients grow their data and digital capabilities, and that we're also growing in parallel.

We have 2000 employees across 36 offices who are focused on accelerating digital transformation. Our state-of-the-art data technologies, lean AI agile methodologies, and cohesive teams of the finest business consultants, data analysts, data scientists, data engineers, and digital experts are all dedicated to bringing extra value to every client. We design data-based solutions to meet our clients' specific needs, always conceived with a business-centric approach and delivered with tangible results. Our data-driven services are built upon the deep AI expertise we've acquired with our 1000+ client base around the globe.

Find out more at artefact.com.

What you will be doing 

Artefact is looking for a Senior AI & Data Scientist: a scientist who owns models end to end, from problem framing through production.

You will work across the full modeling spectrum - forecasting, classification, clustering, and causal analysis on one engagement; fine-tuning experiments and LLM evaluation on the next. You will own your models: the data behind them, the methodology, the deployment, and the story told to the client. You will also help junior scientists grow.

  • Translate business and marketing challenges into analytical use cases with clear hypotheses and success metrics.
  • Develop statistical and machine learning models: regression, forecasting, classification, clustering, and causal inference.
  • Select techniques based on business context, constraints, and data availability - and be able to justify the choice.
  • Ship production-ready solutions: training, deployment, monitoring, and ongoing refinement.
  • Run fine-tuning experiments: dataset curation, SFT, LoRA/PEFT, and rigorous evaluation of results against baselines.
  • Build evaluation suites for LLM systems: benchmarks, LLM-as-judge patterns, and regression tests.
  • Work with embeddings and retrieval where they affect answer quality.
  • Contribute to model selection decisions: prompting vs. RAG vs. fine-tuning, cost vs. quality vs. latency.
  • Build data pipelines for ingestion, transformation, and quality assurance across diverse data sources.
  • Create clear visualizations and dashboards that support data storytelling and decision making.
  • Present findings to client stakeholders, translating methodology into business language.
  • Mentor junior data scientists and use AI-assisted tools (Claude Code, Gemini CLI) to raise the whole team's pace.

What we are looking for 

  • 3-5 years of relevant data science experience with a substantial quantitative skill set.
  • Strong knowledge of statistics and ML algorithms, with at least one proven experience developing and deploying models.
  • Proficiency in Python (scikit-learn, XGBoost; PyTorch a strong plus) and solid SQL.
  • Hands-on experience with LLMs: evaluation, RAG, or fine-tuning experiments (professional or substantial personal projects).
  • Specialization in at least one major AI platform ecosystem - Google (Gemini, Vertex AI), Anthropic (Claude), or OpenAI - and familiarity with a cloud platform (GCP, Azure, or AWS).
  • Comfort with AI-assisted development tools such as Claude Code, Gemini CLI, or Cursor.
  • Excellent interpersonal and communication skills: you can present methodology and results to non-technical audiences.
  • Master's degree (or higher) in statistics/mathematics, engineering, computer science, economics, or a related field, or equivalent experience.

Preferred: 

  • Causal inference, time series, or advanced statistics experience.
  • Hugging Face Transformers, LoRA/PEFT, or open-weight model experience.
  • MLOps exposure: model versioning, pipelines, monitoring.
  • Cloud certifications, especially Google Cloud Professional Machine Learning Engineer.
  •  

Why Join Us

We are united by our values and strengthened by our hybrid expertise.

  • There is always a way: We're from the breed of does, of diggers, of makers. Because ideas are valuable only if executed.
  • Client trust is won on the field: Addressing client needs flows better hands on at their side.
  • If not used, it is useless: Our love for technology translates into a steep desire for adoption, true brilliance is about impact.
  • If not shared, our work is not done: Sharing knowledge is the best way to button up a mission, benefitting clients and colleagues.
  • We learn everyday: Tech is a land where everything moves at the speed of light, you better be ready to challenge yourself.

How We Support Our People

In addition to our values-driven culture, we offer a range of benefits and programs designed to support our employees' growth and well-being, including:

  • Learning and Development: Work alongside a multidisciplinary team of AI, data, and consulting experts who are committed to continuous learning, knowledge sharing, and professional growth.
  • Hybrid Flexibility: Our hybrid work model gives you the flexibility to balance collaboration, client needs, and personal commitments.
  • Comprehensive Benefits: We offer a competitive benefits package that includes medical, dental, and vision coverage, a 401(k) plan with company matching, and paid parental leave.
  • Time to Recharge: We believe sustainable performance matters. That's why we offer unlimited paid time off, giving you the flexibility to take the time you need.
  • Growth Opportunities: As a rapidly growing organization, you'll have the opportunity to expand your skills, take on new challenges, and help shape the future of the company.