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Marshmallow

Junior Pricing Data Scientist

Marshmallow is hiring a Junior Pricing Data Scientist for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Marshmallow.

Level's read on this job

  • Level rates this data scientist role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • This is listed as a remote role.
  • The posting mentions data science, machine learning and statistics among its tools and skills.
  • Marshmallow has 12 other open roles on Level.
  • Posted in the last few days.

AI in this role

Junior Pricing Data Scientist applying quantitative methods and machine learning techniques to insurance pricing problems.

data-sciencemachine-learningstatisticspricing-analytics

We started building Marshmallow in 2017. Since then, we’ve grown from 3 to 700+ people, gained unicorn status, raised ~£140M over three funding rounds, turned profitable, insured millions of drivers and lent millions in car loans.

But we’re only just getting started. Our goal is to become one of the largest financial services providers in the world. Over the next 10 years we’ll grow exponentially, not only by scaling our existing products, but also by building new ones.

To achieve our goals we need incredibly ambitious, commercially driven people who never settle for ‘good enough’. Marshmallowers are hungry for autonomy and ownership, and would rather improve than coast. Everyone raises standards and has an impact, with a focus on collective success over self-interest.

We’ve created an environment where curious, tenacious people win and grow together. If that sounds motivating, this could be the place for you.

London (hybrid, 3 days in office)

We are not able to offer visa sponsorship for this position.

About the team

Marshmallow's pricing team applies quantitative methods to real insurance problems. In this role, you'll learn how pricing decisions work in practice and turn data into meaningful outcomes. You'll work closely with a senior pricing team member, bringing your quantitative background into an industry setting. The aim is for you to develop the practical judgement and independence to take on – and eventually lead – increasingly substantial work.

What you'll be doing

  • Working closely with pricing colleagues on scoped analytical and data-driven projects, taking on more responsibility as your experience develops.

  • Applying mathematical, statistical and machine learning techniques to practical pricing questions, connecting your analysis to the decision it needs to inform.

  • Explaining your findings clearly, keeping colleagues from different areas of the business updated on progress and following up on agreed actions.

  • Using feedback to improve your work and build your understanding of data science methods, coding best practice and the commercial context of insurance pricing.

Who you are

  • You want to learn, seek feedback and build towards working independently.

  • You communicate your rationale and findings clearly, and keep colleagues informed about your progress.

  • You follow through on agreed actions and take ownership of your work.

What you’ll bring

  • A strong academic record and a research master's or PhD in a quantitative field, such as mathematics, physics or econometrics.

  • Experience applying quantitative methods or machine learning to a practical problem through your studies, research, a personal project or work.

  • Industry experience is preferred - a placement, internship or experience whilst studying, or in between research masters / PhD.

  • The ability to explain how your approach fits the problem, rather than relying on theory alone.

Perks of the job

  • Bonus scheme designed to reward high performance

  • Private medical insurance with Vitality, mental health support with Oliva

  • Personal learning budget and 2 dedicated L&D days a year

  • Monthly flexible benefits budget to spend as you choose

  • 25 days holiday plus bank holidays

  • 4 weeks Work From Anywhere per year

Our process

  • Initial call with a member from our Talent Team (30 mins)

  • Combined Past Experience & Technical interview (90 mins)

  • Onsite Culture interviews (90 mins)

Diversity of thought

We know the best ideas come from having different perspectives in the room - and we're committed to hiring fairly, regardless of background, identity or experience. If you see yourself in this role, we'd encourage you to apply.

How we rate this

Junior Pricing Data Scientist at Marshmallow rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data ScienceMachine learningStatisticsPricing Analytics

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$167k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. Tell me about a project where data science was part of your work. What did you do?
  2. Tell me about a project where machine learning was part of your work. What did you do?
  3. Tell me about a project where statistics was part of your work. What did you do?
  4. Tell me about a project where pricing analytics was part of your work. What did you do?
  5. Describe a typical day in a role like this one: which parts run through AI directly?

Adapt your resume

  • List these exact terms on your resume: Data Science, Machine learning, Statistics, and Pricing Analytics. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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