Senior/Lead Data Scientist -Credit & Finance Model Validation

Klarna; Stockholms kommun; Hybrid; SEK 900,000 - 1,200,000; Full time

Job summary

Klarna is seeking a Senior Data Scientist or Lead Data Scientist to join Model Risk within Risk Control. You will review and challenge predictive and AI/LLM models across credit scoring, fraud detection, AML/CTF, finance, and operations, and you’ll help build the code base and AI tooling for scalable validation.

You will work with model developers and business stakeholders to identify control weaknesses, push back on weak modelling practices, and ensure strong governance.

Qualifications

• Hands-on experience building predictive models for credit risk or underwriting.
• Experience assessing provisioning, impairment, and IFRS9 modelling.
• Proficient in Python and SQL for building, running, and interrogating models.

Responsibilities

• Independently review and challenge predictive and AI/LLM models across risk domains.
• Collaborate with model developers and business users to pinpoint control weaknesses and ensure fixes.
• Extend code base and tooling to enable broader, deeper model challenge with limited headcount.
• Prioritize which models and risks to validate based on overall risk exposure.
• Use your own model-building experience to test assumptions and data quality.

Skills

Python

SQL

Model risk

Credit risk modelling

IFRS9 modelling

PySpark

AWS

Stakeholder communication

Independent challenge

Education

Master's degree in a relevant field

Tools

PySpark

AWS

Job description

Klarna, briefly At Klarna, we're building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances. Working here means taking on problems most companies never get to solve, and being hands‑on enough that the interesting part of the work lands with you, not someone else — you'll build with AI, not watch it happen.

Klarna, briefly At Klarna, we're building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances. Working here means taking on problems most companies never get to solve, and being hands‑on enough that the interesting part of the work lands with you, not someone else — you'll build with AI, not watch it happen.

This is the stretch zone. Come find out what you're capable of.

About The Role

Klarna runs more than 200 predictive and AI/LLM models across credit scoring, fraud detection, AML/CTF, finance, and operations — and five people are responsible for independently challenging every one of them. You'd be the sixth, joining model risk within Risk Control to own model risk policy and governance and to review and challenge the models built by teams across the business.

The team, led by Edward McAvoy, has depth in software engineering and model development, but limited experience in credit risk and underwriting, and in provisioning, impairment, and IFRS9 modelling for finance. That's the specific gap this position closes: you'll bring hands‑on model‑building experience in those areas and use it to independently challenge models built by the first line.

Given the ratio of models to reviewers, you won't have time to check every model with equal depth. You'll need to identify which parts of a model carry the most risk and concentrate your challenge there, and you'll help build the code base and AI tooling that lets the team scale its coverage rather than working through the backlog line by line.

What You'll Do

• You'll independently review and challenge predictive and AI/LLM models built by the first line, across credit scoring, fraud detection, AML/CTF, finance (including impairments), and operations.
• You'll work directly with model developers and business users to pinpoint control weaknesses in how models are built and used, and follow through until they're fixed.
• You'll extend the team's code base and AI tooling so more of Klarna's models can be challenged, and challenged more deeply, without adding headcount.
• You'll help decide which models and model risks get the team's limited validation time, prioritizing by risk rather than working through the queue in order.
• You'll draw on your own experience building models to test whether a model's assumptions, data, and methodology hold up, not just whether its documentation does.

Who You Are

• You've hands‑on experience building predictive models for credit risk or underwriting — you've sat on the first line, not just reviewed someone else's work from the second.
• You understand provisioning, impairment, and IFRS9 modelling well enough to assess how a finance model estimates losses, not just how it's coded.
• You're proficient in Python and SQL for building, running, and interrogating models.
• You can work independently with model developers and business stakeholders, challenge their assumptions, and push back on weak controls even when that's an uncomfortable conversation.
• You can prioritize which risks in a model matter most, rather than trying to check everything with equal depth.

Bonus points for

• You've validated or independently challenged models before, ideally from a second‑line risk function.
• You have hands‑on experience with PySpark for distributed data processing.
• You've worked with cloud platforms such as AWS.

Things you should know before applying

• This position is open in both Stockholm and London; you'll work from whichever location you're hired into.
• Depending on your experience, this position will be leveled as Senior Data Scientist or Lead Data Scientist.
• Validation windows for individual models are tight, so you'll need to focus your challenge on a model's riskiest aspects rather than reviewing it end to end.
• Working together: we value co‑located teams; most teams currently meet in the office 2–3 days per week, and this varies by team and can change over time.
• Non‑obvious backgrounds are welcome. Diversity of skills, perspectives and backgrounds is how we create, innovate, and disrupt like no other.
• Final compensation will be based on the candidate's qualifications, skills, and experience.