securitas23d ago

Junior Data Scientist

Sweden·Stockholm / HybridHybridentry

Data ScientistData

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Quick Summary

Overview

Securitas Group Securitas is a world-leading safety and security solutions partner that helps make your world a safer place. By leveraging technology in partnership with our clients, we offer a broad portfolio of value-enhancing services and solutions integrated across the security value chain –…

Key Responsibilities

Machine Learning & GenAI Contribute to the development of LLM-powered pipelines that extract structure and insight from unstructured text - incident reports, operational logs, client data. Support the design and testing of GenAI architectures (e.g.

Requirements Summary

A Degree in a quantitative field (Data Science, Computer Science, Statistics, Engineering, or related) or equivalent practical experience. Solid Python skills and a genuine interest in writing good, maintainable code.

Technical Tools

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Securitas is a world-leading safety and security solutions partner that helps make your world a safer place. By leveraging technology in partnership with our clients, we offer a broad portfolio of value-enhancing services and solutions integrated across the security value chain – from on-site services to advanced monitoring, comprehensive risk prediction and advisory services.

At Securitas, our colleagues show up every day to help keep communities and organizations safe. Our job in the AI team is to make sure they're equipped with the best tools and intelligence possible.

We are Securitas' Specialized AI Team - the internal center of excellence for advanced, custom AI. We don't work on generic tools or off-the-shelf solutions. We build the AI capabilities that require deep technical and domain expertise, and that directly move the needle on how Securitas operates at scale.

You will be joining a small, focused team and growing into a strong technical contributor. You'll work across the full ML lifecycle - from messy data and ambiguous problems to models that run in production and create real operational impact. You'll contribute to meaningful projects from day one, with guidance and mentorship from senior team members. Over time, you'll take on increasing ownership as you build your skills and confidence.

Responsibilities

Machine Learning & GenAI

Contribute to the development of LLM-powered pipelines that extract structure and insight from unstructured text - incident reports, operational logs, client data.

Support the design and testing of GenAI architectures (e.g. RAG), prompt strategies, and evaluation frameworks, learning what "good" looks like in production.

Training and evaluation och machine learning models - classification, regression, and forecasting.

Assist in building and iterating on workforce management models - demand forecasting, shift scheduling optimization, and attrition modeling.

Help develop client churn models that surface early, actionable retention signals for the business.

Engineering & Delivery

Work across the end-to-end ML lifecycle: data exploration, feature engineering, modelling, evaluation, and deployment support.

Write clean, readable, and testable Python code - and keep improving your software engineering habits with support from the team.

Learn and apply MLOps fundamentals: model serving, monitoring, CI/CD, Docker, and reproducible pipelines.

Use modern AI coding tools (GitHub Copilot, Claude Code) to work more productively and iterate faster.

Collaboration & Communication

Translate business questions into data problems, with guidance from senior data scientists.

Present findings and model results to internal stakeholders in a clear, honest, and accessible way.

Work collaboratively across data, engineering, and business teams.

Must-haves

A Degree in a quantitative field (Data Science, Computer Science, Statistics, Engineering, or related) or equivalent practical experience.
Solid Python skills and a genuine interest in writing good, maintainable code.
Hands-on experience with core ML concepts and NLP - whether from coursework, personal projects, internships, or early-career work.
Familiarity with LLMs: you've worked with them beyond basic prompting and are curious about how they fail and how to evaluate them.
A scientific, evidence-based mindset - you ask "why?" when results look surprising, and you check your own assumptions.
Clear, honest communication: you can explain a method, a result, or a limitation in plain language.

Nice-to-haves