Senior Data Scientist

Artefact US•New York, NY

10h•Hybrid

About The Position

Artefact is a consulting firm that transforms data into measurable value and business impact. With over 2,000 employees and 36 offices globally, Artefact specializes in data, analytics, and AI consulting. The company is expanding its US presence with offices in NYC and Los Angeles and is seeking a Senior Data Scientist to join its founding US team. This role involves defining world-class data science standards and contributing to client engagements. As a Senior Data Scientist, you will be the technical lead for complex client projects, moving between data exploration, model development, and executive communication. You will translate scientific rigor into business solutions and ensure models are implemented and scaled for real-world impact. This role requires embedding with clients, co-owning outcomes, and ensuring models function effectively in production. Additionally, you will serve as a technical anchor for the US team, establishing standards, mentoring junior staff, and advancing Artefact's methodologies.

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