Senior Data Scientist
About The Position
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've launched in the US with offices in NYC and Los Angeles — and we're looking for a Senior Data Scientist to help define what world-class data science looks like on our founding team. 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.
Requirements
- 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)
- Demonstrated ability to lead technical workstreams and mentor junior team members
- Consulting or client-facing experience is highly desirable; the ability to manage ambiguity, scope problems, and deliver under pressure is essential
Nice To Haves
- 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
- Exposure to marketing analytics, customer analytics, or demand forecasting in a consumer-facing industry is a meaningful asset
Responsibilities
- 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 practice
- Mentoring junior data scientists and analysts, actively investing in the team's technical depth and growth
Benefits
- competitive benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
