Hiring nowData science, analytics and decision support

Data Scientist

Also called Applied Data Scientist

A data scientist frames the questions a company needs answered, builds validated models and analyses, and says plainly what the evidence does and does not support. That keeps product and commercial decisions grounded in something testable.

What they do

Starts with the decision, not the dataset. Works with product, operations or commercial colleagues to turn a vague question into something measurable. Pulls and cleans data with SQL and Python, explores it, and chooses a method that fits the problem, whether that is a regression, a classifier or a designed experiment. Checks assumptions, tests the model on data it has not seen, and writes code others can rerun. Then explains the result in plain language, including where it is weak, so the people deciding know how far to trust it.

What a strong hire delivers

Within the first months, a strong hire has taken one or two live questions from framing through to a reproducible analysis or model that someone acted on. They have cleaned up how the team measures results, flagged where existing numbers mislead, and left documented code and clear write-ups. Colleagues start bringing them questions earlier, before the data is collected.

How you can tell they are good

Ask them to walk through one piece of past work from the question to the decision. Listen for how they chose the method, how they validated it and what they told stakeholders about its limits. Ask what they would do with messy or biased data. Strong people name trade-offs and uncertainty readily. Weak ones lead with tools and accuracy scores.

Why it is hard to fill

The title covers very different jobs, so candidates with the same label can have little in common. Some are analysts who build dashboards, others are close to machine learning engineers. Real statistical judgement, paired with the ability to talk to non-specialists, is less common than the CVs suggest. Many applicants are strong on tooling and thin on inference, so screening needs to test reasoning, not libraries.

Where to find them

Look at analysts who have moved into modelling, applied scientists, and people from statistics, economics, physics, biology or social science who work with data daily. Product analytics and experimentation teams are rich sources, as are health, finance, logistics and marketplace companies. Research groups in universities also produce candidates, though check they have shipped work that someone used.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Data Scientist

  1. Tell me about an analysis that changed a decision. How did you frame the question at the start?

    A strong answer names the decision, who owned it and how the question was narrowed into something measurable. They explain what changed as a result. Worry if the story is about technique with no decision attached.

  2. How do you check that a predictive model will hold up on data it has not seen?

    Look for sensible train and test separation, awareness of leakage, time-based splits where relevant, and checks on stability across groups. Worry if the only answer is a single accuracy figure on one split.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a data scientist to help us make better decisions with evidence. You will work with product, operations and commercial colleagues to frame questions, build validated models and analyses, and explain clearly what the results do and do not tell us. We value sound reasoning and honest communication.

What you will do

  • Turn open business questions into clear, measurable analytical problems
  • Build and validate statistical and predictive models
  • Design and analyse experiments
  • Write reproducible code and documentation others can rerun
  • Communicate findings, limits and recommendations to non-specialists
  • Work with engineers to get useful models into use
  • Improve how we define and measure success

What you will bring

  • Demonstrated work taking a question through to a decision
  • Strong Python and SQL, shown in real projects
  • Solid grounding in probability, statistics and experimental design
  • Clear approach to validating models and spotting leakage
  • Ability to explain uncertainty to a non-technical audience
  • Habit of writing reproducible, reviewable analysis

Nice to have

  • Experience with R
  • Experience in causal inference methods
  • Experience building dashboards or visualisations for decision makers

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • SQLAdvancedQuery and validate relational data
  • PythonWorkingDevelop tested numerical and data software
  • Experimental designWorkingChoose comparisons that identify credible effects

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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