Data Scientist Role

Data Scientists turn difficult questions and complex data into evidence. They work with stakeholders to define what should be measured, determine whether the available data can support the question, select appropriate statistical or machine learning methods, and explain what the results do and do not establish.

The work runs from exploratory analysis through model development, validation, interpretation, and communication. Data Scientists examine data quality and bias, test assumptions, quantify uncertainty, compare alternatives, document methods, and make analyses reproducible. When a model will be used repeatedly, they help define how its performance should be assessed over time.

What you’ll build

• Forecasting, classification, risk, anomaly-detection, segmentation, causal, simulation, or optimization models matched to the question and available evidence.
• Exploratory analyses that reveal distributions, relationships, outliers, missingness, and limits in the data before formal modeling begins.
• Experimental or quasi-experimental designs, sampling plans, measurement strategies, and evaluation frameworks.
• Reproducible analytical pipelines, notebooks, code, data documentation, model cards, and validation reports that allow others to follow the work.
• Decision briefings and analytical products that communicate results, uncertainty, assumptions, limitations, and appropriate uses to technical and nontechnical audiences.

Who you are

You are rigorous about methods and candid about uncertainty. You would rather narrow a claim than overstate the evidence, and you are willing to report a null or inconvenient result when that is what the analysis supports.

You are curious about the domain, not only the dataset. You work with subject-matter experts, analysts, engineers, and decision-makers to frame the right question, challenge assumptions, and turn technical results into conclusions people can use responsibly.

• A working foundation in statistics, probability, research design, ML, optimization, or another quantitative discipline relevant to the opening.
• Ability to prepare, explore, and analyze data using tools such as Python, R, or SQL, with attention to quality and provenance.
• Experience selecting methods, validating assumptions, comparing models, investigating errors, and interpreting results in context.
• Reproducible practice through documented code, version control, peer review, traceable data transformations, and clear analytical records.
• The communication judgment to explain uncertainty, bias, limitations, and appropriate use without hiding the central finding.