Staff Data Scientist

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there. We’ve earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship. Position Overview As a Staff Data Scientist, you own the modeling work behind our hardest AI and machine learning problems: framing ambiguous business questions, building and validating models, and getting them into production. You bring deep applied ML and statistical rigor, the engineering judgment to ship at scale, and the credibility to explain it all to a room of executives. You’ll work alongside scientists, data engineers, developers, and product partners across client engagements, with real autonomy over technical direction. You set the standard for scientific rigor, mentor junior data scientists, and thrive where the problem is ambiguous, the data is messy, and the answer has to work in production. Why This Role Matters Staff Data Scientists are where Robots and Pencils’ promise of “production-ready AI in 30 to 45 days” is backed by evidence. On the engagements you lead, you decide which approach is worth building, how success gets measured, and whether a model is ready for real users. The work moves only as fast as someone is willing to own the science, test the hypothesis, and ship the result. What You’ll Do Craft & Delivery

Design, build, and evaluate predictive and prescriptive models across domains such as customer behavior, operational efficiency, and personalization

Apply machine learning, deep learning, and statistical techniques to solve real-world business challenges

Drive end-to-end ML and GenAI projects with a high degree of ambiguity, scale, and complexity, from proof of concept through production

Build, optimize, and deploy models into production, working with scientists and software engineers to deliver solutions that last

Establish scalable, automated processes for large-scale data analysis, model development, validation, and serving

Run A/B and multivariate experiments and apply causal inference to validate hypotheses and measure impact

Collaboration & Communication

Analyze large, complex datasets and translate what you find into clear, strategic recommendations

Explain scientific methodology and results to technical and non-technical audiences, using data visualization that makes the point

Partner with product managers and business stakeholders to identify opportunities and frame data science solutions

Work closely with data engineers, analysts, and software developers to build scalable, data-powered applications

Leadership & Influence

Serve as the technical practitioner in AI/ML, leading efforts within your team and across other teams

Research new and innovative science approaches and bring the best of them into client work

Mentor junior data scientists, supporting their technical development and scientific rigor

Build reusable assets, tools, and processes that increase team velocity and impact

What You’ll Bring

5+ years of professional experience in data science, statistics, and applied machine learning, including 3+ years building models for business applications

PhD, or a Master’s degree with 4+ years of experience, in Computer Science, Computer Engineering, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field

Deep proficiency in Python or R, with strong skills in libraries like scikit-learn and TensorFlow/PyTorch, and fluency in models, algorithms, and ontologies

Experience programming in Java, C++, or a related language

Proven track record in statistical modeling, time series forecasting, NLP, or optimization

Experience in one or more of: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high-performance computing