Junior ML/Software Engineer

Werlabs AB

Junior Data Scientist – Werlabs Om Werlabs Werlabs är ett medtech-bolag med en tydlig mission: Lev bättre, längre. Vi hjälper våra användare att identifiera och motverka risker för sin långsiktiga hälsa – långt innan de blir sjuka. Vårt huvudsakliga fokus är att minska risken för kroniska sjukdomar, framför allt kardiometabola sjukdomar, genom preventiv hälso- och sjukvård snarare än att vänta och behandla sjukdomar först när de väl har inträffat. Om rollen Som Junior Data Scientist blir du en del av vårt tech-team i Stockholm. Du arbetar nära kliniska och tekniska kollegor med att omvandla komplex hälsodata till verkligt värde för våra användare. Exempel på arbetsuppgifter: Bygga och förbättra datapipelines för biomarkör- och bilddata (t.ex. HL7-flöden för DEXA-resultat) Utveckla modeller och verktyg som översätter tekniska medicinska underlag till patientvänlig information Analysera kliniska datamängder för att stödja produktutveckling och kvalitetssäkring Samarbeta med läkare, produkt och ingenjörer för att säkerställa att lösningar är både tekniskt robusta och kliniskt relevanta Vi söker dig som har Examen inom data science, maskininlärning, statistik, teknik eller motsvarande Goda kunskaper i Python och SQL Förmåga att arbeta strukturerat med stora och komplexa datamängder God kommunikationsförmåga på engelska Starkt meriterande Medicinsk eller klinisk bakgrund (t.ex. läkar-, sjuksköterske- eller biomedicinsk utbildning). En stor del av vårt arbete sker på kliniska data, och domänkunskap inom medicin är en tydlig tillgång i rollen. Erfarenhet av att arbeta med hälsodata, journalsystem eller standarder som HL7/FHIR Hands-on-erfarenhet av frontier-modeller inom generativ AI samt av att bygga agentiska arbetsflöden Kunskaper i svenska Erfarenhet av modern ML-tooling och molntjänster Vi erbjuder En nyckelroll i ett bolag som växer snabbt inom preventiv hälsa Möjlighet att arbeta i skärningspunkten mellan medicin, data och teknik Centralt belägna kontor i Stockholm Omfattning och placering Heltid, tillsvidareanställning. Placering i Stockholm. Ansökan Skicka din ansökan med CV via epost till rekrytering@werlabs.com. Urval sker löpande och tjänsten kan komma att tillsättas innan ansökningstiden gått ut.

Klarna Bank AB

What you will do Perform independent end-to-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models. Review and challenge first-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit-learn, LightGBM, graph models, anonaly detection techniques, GenAI components). Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies. Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, fraud rate capture, business impact trade-offs). Evaluate model stability, drift detection, retraining strategies, and production monitoring practices. Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations. Review large-scale transaction datasets and feature pipelines (e.g., >100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias. Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy. Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI-based systems. Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda). Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python-based validation libraries). Collaborate closely with first-line fraud data scientists, ML engineers, product, and business stakeholders to ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and external regulatory expectations. Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management. Who you are Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering. 3+ years of hands-on experience in fraud-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc). Strong expertise in machine learning methods used in fraud detection, including tree-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques. Deep understanding of the end-to-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage. Strong programming skills in Python and SQL; experience with PySpark/Spark and large-scale data processing. Experience building agentic AI workflows. Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows. Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations. Experience assessing model bias, fairness, explainability, and privacy risks. Excellent analytical thinking and structured problem-solving skills, with the ability to assess complex models and clearly articulate risks and limitations. Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non-technical audiences. Ability to work independently while constructively challenging first-line teams in a collaborative manner. Awesome to have Experience in BNPL, credit cards, payments, or other transaction-heavy financial products. Experience validating models in highly regulated environments. Experience mentoring junior validators or leading validation reviews. Exposure to inference of rejected transactions and understanding of fraud/credit overlap. Familiarity with AI governance frameworks and emerging AI regulatory requirements.

Deer Data AB

About Deerdata Deerdata AB is a Stockholm-based data & AI consultancy. We design and build modern data platforms, analytics solutions, and AI capabilities for clients across automotive, telecommunications, retail, and business process outsourcing industries. We're a tight team with short decision paths and a collaborative engineering culture. The role We're hiring a Senior Data Engineer to build and maintain data pipelines and platform infrastructure for our clients. You'll work with modern tooling, lead technical delivery, and help shape our data engineering practice. What you'll do: Design and develop data pipelines. Build and maintain dbt transformation models. Work with Azure or similar data services. Manage infrastructure with Infrastructure as Code (Terraform, Bicep, or equivalent). Implement data quality, monitoring, and CI/CD for data workflows Collaborate with analysts, architects, and client stakeholders Apply AI/ML and GenAI techniques to data platforms — from automation and intelligent pipelines to LLM-powered tooling and agentic workflows Mentor junior engineers and contribute to technical direction What we're looking for: 5–10 years of data engineering experience Consulting experience — comfortable in client-facing roles, delivering on engagements Deep expertise in Azure cloud data services or similar (AWS, GCP). Strong proficiency with Databricks and Apache Spark or similar (Snowflake, BigQuery, Redshift etc). Advanced SQL and Python skills Experience with Scala for Spark development Proficiency in dbt (or equivalent transformation frameworks) Experience with Infrastructure as Code (Terraform, Bicep, ARM, or similar) Solid understanding of data modelling and warehouse design Experience with cloud platforms (AWS, GCP) — multi-cloud familiarity valued Interest and experience in AI/ML and GenAI — LLMs, intelligent automation, agentic workflows, or MLOps Fluent English (Swedish a plus) What we offer: Start: August/September, 2026 (or by agreement) Type: Full-time, permanent (tillsvidare) Location: Solna, Stockholm