Staff Data Scientist

Robots and Pencils

About The Position

Robots & Pencils is an applied AI engineering firm that builds AI co-workers for enterprise operations. The company combines UX and engineering capabilities, works primarily with AWS, and serves clients across industries including consumer products and retail, education, energy, financial services, healthcare, and manufacturing. Founded in 2009, it has delivery centers in Canada, the United States, Eastern Europe, and Latin America. Its teams average more than 15 years of experience. As a Staff Data Scientist, you own modeling for complex AI and machine learning problems, from framing ambiguous business questions and validating models to deploying them in production. You bring applied machine learning and statistical rigor, engineering judgment, and the ability to communicate findings to executives. You collaborate with scientists, data engineers, developers, and product partners across client engagements, with autonomy over technical direction. You set standards for scientific rigor, mentor junior data scientists, and work effectively with ambiguous problems and messy data. The role supports the company’s aim of delivering production-ready AI in 30 to 45 days. You determine which approaches merit development, how to measure success, and whether models are ready for real users. The position also involves leading AI/ML work, contributing scientific approaches to client engagements, and building reusable assets and processes that improve team velocity and impact.

Requirements

At least 5 years of professional experience in data science, statistics, and applied machine learning, including at least 3 years building models for business applications.
PhD, or a Master’s degree with at least 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, strong skills with libraries such as scikit-learn and TensorFlow/PyTorch, and fluency in models, algorithms, and ontologies.
Experience programming in Java, C++, or a related language.
A 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.
Experience designing and analyzing controlled experiments, such as A/B testing and uplift modeling.
Demonstrated success deploying models into production using APIs, pipelines, or ML frameworks.
Hands-on experience with Databricks, AWS SageMaker, and Snowflake.
Experience building and deploying Data Mesh architectures.

Nice To Haves

Palantir experience.
AWS cloud certifications or ML specialty credentials.

Responsibilities

Design, build, and evaluate predictive and prescriptive models in areas such as customer behavior, operational efficiency, and personalization.
Apply machine learning, deep learning, and statistical techniques to business challenges.
Drive end-to-end ML and GenAI projects, from proof of concept through production, including work with ambiguity, scale, and complexity.
Build, optimize, and deploy production models in collaboration with scientists and software engineers.
Establish scalable, automated processes for data analysis, model development, validation, and serving.
Run A/B and multivariate experiments, and apply causal inference to test hypotheses and measure impact.
Analyze large, complex datasets and turn findings into clear, strategic recommendations.
Explain scientific methodology and results to technical and non-technical audiences, using effective data visualization.
Partner with product managers and business stakeholders to identify opportunities and frame data science solutions.
Work with data engineers, analysts, and software developers to build scalable, data-powered applications.
Lead AI/ML efforts within the team and across other teams as a technical practitioner.
Research innovative scientific approaches and bring suitable approaches into client work.
Mentor junior data scientists and support their technical development and scientific rigor.
Build reusable assets, tools, and processes that increase team velocity and impact.