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A good data scientist can execute a well-defined analysis. A great one also asks whether the problem, data, metric, and method are right—and helps people use the answer. That does not mean every exceptional practitioner has the same strengths: insight, modeling, platform work, broad execution, and team leadership are distinct contributions.
The 18 differences below are a practical synthesis of published perspectives on data-science work, not a verified list from Thomas C. Redman’s 2013 Harvard Business Review article, “What Separates a Good Data Scientist from a Great One”. They describe habits to develop, not a universal scorecard.
Problem definition and context
1. Takes the request literally → finds the decision behind it
A request such as “build a churn model” describes an output, not necessarily the problem. A stronger practitioner asks what decision the model is meant to improve, who will act on it, and what would change if the answer were known. Berthold’s 2019 account distinguishes clearly scoped work from work that first requires problem formulation and stakeholder discussion (HDSR).
2. Starts with whatever data exists → asks what data is needed
Available data is not automatically sufficient data. Clarifying the decision may reveal a missing population, time period, outcome, or source. The analyst should help identify what needs to be collected or joined before treating a convenient dataset as the answer.
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3. Treats domain expertise as optional → works with domain experts
Statistical and computational methods do not supply all the context needed to interpret a result. Domain experts can explain how records are generated, what a variable means in practice, and which apparent patterns may reflect workflow rather than the underlying phenomenon. The PNAS perspective on data science emphasizes statistical, computational, and human perspectives (PNAS).
4. Accepts the success metric → checks whether it reflects real costs
A metric can reward the wrong behavior when different errors have different consequences. For example, a false alarm and a missed case may carry very different costs. Before optimizing a score, establish which errors matter to the decision and how the metric represents them.
5. Assumes the sample represents everyone → checks who is missing
A dataset can describe its source population well while saying little about people outside it. Berthold notes that existing customers may be a poor basis for recommendations aimed at entirely new prospects. Check how the data was collected, whose outcomes are observable, and which groups are absent or represented differently.
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6. Treats a clean benchmark as the whole job → anticipates real-world data traps
Production and field data may be incomplete, inconsistent, delayed, or shaped by the process that generated it. Sourcing, blending, transformation, and quality checks are part of applied analysis, not just preliminaries to modeling. A benchmark result is useful only to the extent that its data conditions resemble the intended use.
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Analytical judgment and craft
7. Reaches for a familiar algorithm → chooses a method for the question
No single algorithm family defines data science. The right approach depends on the question, available evidence, constraints, and intended use. Data science spans multiple methods and disciplines rather than one modeling recipe, as discussed in the field overview by Peng and Parker (Annual Review of Statistics and Its Application).
8. Treats automation as an answer → knows when judgment is needed
Automated optimization can be effective for standard, well-specified tasks. It cannot decide by itself whether the target is meaningful, the data is suitable, or the task has been framed correctly. Open-ended work may require creativity, domain input, and methodological judgment beyond selecting the best automated score.
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9. Optimizes one score → interprets it in context
A performance number is evidence about a particular dataset, setup, and metric—not a complete account of usefulness. Explain what the score captures, what it leaves out, and how errors would affect the decision. A higher score is not automatically better if it comes at an unacceptable cost elsewhere.
10. Treats cleaning as overhead → treats preparation as analytical work
Transforming and combining data can change what is measurable and what conclusions are justified. Keep track of definitions, exclusions, joins, and transformations; otherwise a polished model may rest on a distorted input. Data preparation is part of the reasoning, not merely a chore before it begins.
11. Looks only for confirmation → investigates anomalies
An unexpected pattern may indicate a data defect, an unrecognized process, or a real phenomenon worth explaining. Rather than dismissing it or immediately declaring a discovery, check whether it is reproducible, inspect the data-generating context, and formulate a testable explanation. Berthold describes expert exploratory work as a setting where anomalies can prompt new hypotheses (HDSR).
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12. Runs one analysis → iterates as understanding changes
Analysis is often a cycle: examine data, revise the question, test an approach, discuss findings, and refine the work. Feedback can change what both the analyst and domain expert think the problem is. Peng and Parker foreground iterative data analysis as a central theme of the discipline (Annual Review of Statistics and Its Application).
13. Presents an opaque result → makes the work inspectable
Others should be able to understand how the data became a result and, where appropriate, reproduce the analysis. Clear records of inputs, decisions, code, and assumptions make review and correction possible. Reproducibility and systems engineering are among the themes in Peng and Parker’s account of data science.
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14. Reports model performance → explains the decision it supports
Translate technical findings into their implications: what the result suggests, what action it could inform, and what uncertainty remains. Data science aims to support decisions or develop knowledge; explaining the result is part of that work, not an optional presentation layer (PNAS).
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15. Works in isolation → builds shared understanding
Stakeholder conversations can uncover constraints, clarify what counts as a useful answer, and expose missing context in the data. Communication should therefore happen while framing and conducting the analysis, not only when presenting a finished result. Collaboration is particularly important when the request, population, or outcome is ambiguous.
16. Stops at a notebook or prototype → considers delivery and operations
Where a project requires a live application or recurring decision, the work may extend to deployment, monitoring, and updates. A result can lose value as data, users, or conditions change. Treat operational responsibilities as part of the project when they are in scope; not every data-science role owns production systems.
17. Treats impact as an individual model → improves how the team uses data
A practitioner can have influence beyond a single analysis by helping colleagues develop shared practices and turn evidence into action. Microsoft Research’s 2015 study of data scientists across product groups described team leaders as one of five working styles (Microsoft Research). That finding describes a role in the study, not a requirement for every individual.
18. Assumes greatness looks the same in every role → calibrates excellence to the work
In the same Microsoft Research report, the five styles were Insight Providers, Modeling Specialists, Platform Builders, Polymaths, and Team Leaders. They represent different ways of contributing; a modeling specialist need not also be a platform builder or team leader to do excellent work. Judge the contribution against the responsibilities and outcomes of the role, not an imaginary all-purpose ideal.
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These distinctions are better treated as a development map than a ranking. Berthold describes novice, apprentice, and expert activities: a well-scoped task may center on model optimization, while less-defined work can demand stakeholder-facing formulation, exploration, and hypothesis testing. The progression is conceptual, not a certification ladder, and practical experience matters.
- For a defined modeling assignment, focus on sound methods, careful data preparation, valid evaluation, and inspectable work.
- For an ambiguous business or research question, invest more effort in framing, representation, domain collaboration, and choosing meaningful evidence.
- For work that informs recurring decisions, clarify delivery and monitoring responsibilities alongside the analysis.
- For a specialized role, deepen the skills that match its contribution rather than trying to embody every working style.
No verified statistic establishes a general performance gap between “good” and “great” data scientists. The useful distinction is qualitative: excellence combines technical craft with judgment about what to analyze, how to interpret it, and how the result can responsibly be used.
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