About the Job

Do you get excited when a messy, ambiguous business problem finally yields to the right model? Do you think in systems, speak fluently across the technical-business divide, and want your work to do more than sit in a notebook — you want it to actually ship, scale, and matter?

With over 2,000 employees, 36 offices on five continents, and world-class clients like Samsung, L'Oréal, and Mattel, Artefact is a consulting firm that transforms data into measurable value and business impact. We're growing our Canadian presence and want you to join us as a technical cornerstone of our founding team in Canada.

Who We Are

Founded and headquartered in Paris, Artefact is a next-generation consulting firm specializing in data, analytics, and AI consulting — dedicated to transforming data into business impact across the entire value chain of organizations. We don't just advise; we build, implement, and deliver results our clients can measure.

We have 2,000 employees across 36 offices focused on accelerating digital transformation for some of the world's most recognizable brands. Our state-of-the-art data technologies, lean AI agile methodologies, and cohesive teams of elite business consultants, data analysts, data scientists, data engineers, and digital experts are all laser-focused on delivering real value to every client. We design data-driven solutions tailored to each client's specific needs — always conceived with a business-first mindset and delivered with tangible, measurable results. Our expertise is built on deep AI knowledge acquired through 1,000+ client engagements across the globe.

Find out more at artefact.com.

What You Will Be Doing

As a Senior Data Scientist, you'll be the technical engine behind some of our most complex and consequential client engagements in Canada. You'll move fluidly between data exploration, model development, and executive communication — bringing scientific rigor to business problems and translating results into strategies that clients actually implement.

This isn't a role where you hand off findings and walk away. You'll be embedded with clients across Canada's most dynamic sectors — financial services, retail, CPG, telecoms, media, and the public sector — co-owning outcomes and ensuring that the models you build don't just perform in a test environment, they create real, lasting impact in production. You'll also be a technical anchor for our Canadian team, setting standards, mentoring junior data scientists, and contributing to the methodologies that define Artefact's edge in this market.

Key responsibilities include:

• Designing and building end-to-end machine learning and statistical models that solve high-stakes business problems — from framing the question to deploying the solution
• Conducting rigorous exploratory data analysis to uncover patterns, anomalies, and opportunities that inform both technical and strategic decisions
• Translating complex model outputs and analytical findings into clear, compelling narratives for senior client stakeholders — making the technical accessible without dumbing it down
• Partnering with client teams and data engineers to ensure models are production-ready, scalable, and built on clean, reliable data pipelines
• Defining the analytical approach for client engagements — selecting the right methods, tools, and frameworks for the problem at hand, not just the ones you're most comfortable with
• Contributing to new business proposals — helping articulate Artefact's technical capabilities and translating data science into clear client value
• Developing thought leadership and internal methodologies — publishing research, building reusable frameworks, and raising the technical bar across the Canadian practice
• Mentoring junior data scientists and analysts, actively investing in the team's technical depth and growth

What We Are Looking For

We want someone who is as comfortable whiteboarding a modeling strategy with a client's Chief Analytics Officer as they are debugging a pipeline before a critical delivery. You've shipped models that people actually use. You've sat in rooms where the business stakes were real, and you've delivered. You know the difference between a technically elegant solution and a practically useful one — and you always choose useful.

You'll arrive ready with the following:

• 4–7 years of hands-on experience in data science, machine learning, or advanced analytics — with a demonstrable track record of end-to-end model delivery in a client-facing or high-stakes business environment
• Advanced degree (MSc or PhD) in a quantitative field — statistics, mathematics, computer science, engineering, or equivalent; strong undergraduate candidates with exceptional experience will be considered
• Expert-level proficiency in Python and/or R; you write clean, maintainable, production-quality code
• Deep expertise in machine learning and statistical modeling — regression, classification, clustering, time series, NLP, recommendation systems, and/or deep learning, depending on your specialization
• Strong command of SQL and experience working with large-scale datasets across cloud platforms (GCP, AWS, or Azure)
• Experience with MLOps practices — model versioning, monitoring, deployment pipelines, and productionization — is a significant differentiator
• Exceptional communication skills — you can explain a gradient boosting model to a CFO and a business case to an ML engineer, and both conversations land
• Demonstrated ability to lead technical workstreams and mentor junior team membersBilingualism (English/French) is a significant asset, particularly for client engagement in Quebec and federal government contexts
• Consulting or client-facing experience is highly desirable; the ability to manage ambiguity, scope problems, and deliver under pressure is essential
• Familiarity with Canada's data privacy regulatory landscape — PIPEDA, Quebec's Law 25, and sector-specific frameworks in financial services and healthcare — is a meaningful differentiator
• Exposure to marketing analytics, customer analytics, fraud detection, risk modeling, or demand forecasting within Canadian financial services, retail, or CPG is a strong asset

Why You Should Join Us

At this level, you're not just looking for a job — you're looking for the right environment to do your best work. Here's what makes Artefact different:

Variety that keeps you sharp. In a product role, you go deep on one problem. Here, you'll work across Canada's most dynamic industries and business functions — building the kind of range that makes exceptional data scientists truly exceptional. No two engagements are the same.

Your work actually ships. We have a relentless focus on adoption and impact. The models you build are designed from day one to live in production, influence decisions, and create measurable value — not to gather dust in a final report.

A global technical community, with local roots. With 2,000 data and AI specialists across 36 offices worldwide, you'll have access to some of the most sophisticated data science thinking on the planet — while building something meaningful right here in Canada. You'll bring global best practices to the Canadian market and contribute Canadian insights back to the world.

Founding team energy. We're building our Canadian practice, which means the standards, culture, and technical identity you help create now will define Artefact in this market for years to come. That's a rare opportunity at any stage of a career.

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

There is always a way — We're builders, diggers, and makers. Ideas only count if they get executed.
Client trust is won in the field — We show up where it matters, working side by side with our clients to solve what's real.
If not used, it is useless — We build for adoption and impact. True brilliance is measured by what changes, not what's delivered.
If not shared, our work is not done — Sharing knowledge is how we close the loop — for our clients and for each other.
We learn every day — In a field moving at the speed of light, intellectual curiosity isn't optional. It's survival.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

The Role

This is a full-ownership data engineering role at the center of Medeloop's AI platform. You won't be maintaining pipelines someone else built,  you'll be architecting the data backbone that powers AI agents doing real operations at scale. You'll work directly with data scientists, AI engineers, and product teams to turn raw, complex healthcare data into the clean, structured, semantically-rich foundation our AI scientists depend on. Your work shows up in customer products and research outcomes, not internal dashboards that no one reads.Candidates who currently perform these tasks exclusively through manual processes are unlikely to be suitable for this role. We require an individual who has already adopted and integrated AI techniques to enhance operational velocity, rather than one who is contemplating future experimentation.If you want to build something that genuinely changes how medical research gets done, this is the role.

What You'll Own

• The healthcare data lake: curating, extending, and evolving it through new concepts, derived variables, and data models that directly inform our AI engines and customer products
• AI-native data workflows: designing and operating AI-powered pipelines (using tools like Claude Code and agent frameworks) to automate harmonization, cleaning, quality checks, and summarization at scale
• NLP and semantic infrastructure: building pipelines for entity extraction, concept normalization, embedding-based retrieval, and semantic search that power the AI Scientist platform
• Novel data extraction approaches: experimenting with and building new methodologies for working with unstructured clinical data, not just applying existing playbooks
• Research-grade data products: delivering analytical samples, cohorts, and final datasets that withstand scientific scrutiny and are actively used by researchers and customers
• Data governance and observability protocols: including access controls, PHI/PII handling, data classification, compliance, monitoring, alerting, data freshness, and comprehensive documentation to enable self-service capabilities.

What We're Looking For

• 3+ years of relevant data engineering or data management within an analytics-driven organization, with end-to-end ownership from raw ingestion to final data product
• Deep hands-on experience with healthcare CDMs (OMOP, FHIR, PCORnet) — designing or extending them, not just querying
• Knowledge of medical ontologies: UMLS, SNOMED CT, RxNorm
• Experience with big data, data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, embedding workflows, and other AI/ML workloads. Experience in big data tooling such as Spark, Iceberg, EMR
• Fluent in Python and SQL; comfortable across structured and unstructured data
• Proven NLP experience: semantic search, entity recognition, concept normalization, embedding pipelines
• Strong grasp of inferential statistics and cohort methodology to be a real partner to data scientists and customers (as part of onboarding)
• Experience contributing to an AI/ML product, especially in automated research or scientific discovery
• Experience mentoring other engineers and providing technical leadership

Bonus Points

• Multi-cloud experience (AWS, Azure, GCP)
• Authorship or contribution to peer-reviewed publications or technical reports

Why Medeloop

• Ownership from day one: small team, high-trust, no layers between your work and its impact
• Technically ambitious: you'll build AI-powered workflows, not just support them
• Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
• Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build