Machine Learning Specialist
⚲ Stockholm City
Do uzgodnienia
Wymagania
- Data analysis
- Deployment
- Machine Learning (ML)
- Architectural (Engineering)
- Security Management (Workplace)
- Use Cases
- maintenance
- Network
- Prototyping
- TensorFlow
Opis stanowiska
Introduction & Summary
The Radius squad is tasked with overseeing and enhancing impactful machine learning (ML) initiatives aimed at subscription metrics such as subscriber counts, monthly active users, and premium retention. This role is tailored for those skilled in machine learning engineering, model retraining, and model-driven decisioning, seeking to drive revenue through innovative ML solutions.
Main Responsibilities
As a Machine Learning Engineer (MLE) embedded within the Gyro / Payments pod, you will spearhead and manage live ML workstreams, ensuring operational excellence and exploratory innovation.
• Maintain and enhance ML Routing through ongoing retraining and recalibration.
• Improve ML Chaining by implementing model-driven decisions based on decline reasons.
• Optimize ML Network Token efficacy in conjunction with retry strategies.
• Expand the ML action space with novel approaches to transaction handling.
• Collaborate on joint retry and routing optimization initiatives.
Key Requirements
• Machine Learning Engineering
• Machine Learning Model Retraining
• Model-Driven Decisioning
• Predictive Modeling
• Exploratory Data Analysis
• Bandit Algorithms
• TensorFlow
• Model Deployment
• Collaboration
• Kubeflow
• Hendrix
Start/End: 2026-06-22 to 2026-12-21
Workplace: Sweden
The Radius squad is tasked with overseeing and enhancing impactful machine learning (ML) initiatives aimed at subscription metrics such as subscriber counts, monthly active users, and premium retention. This role is tailored for those skilled in machine learning engineering, model retraining, and model-driven decisioning, seeking to drive revenue through innovative ML solutions.
Main Responsibilities
As a Machine Learning Engineer (MLE) embedded within the Gyro / Payments pod, you will spearhead and manage live ML workstreams, ensuring operational excellence and exploratory innovation.
• Maintain and enhance ML Routing through ongoing retraining and recalibration.
• Improve ML Chaining by implementing model-driven decisions based on decline reasons.
• Optimize ML Network Token efficacy in conjunction with retry strategies.
• Expand the ML action space with novel approaches to transaction handling.
• Collaborate on joint retry and routing optimization initiatives.
Key Requirements
• Machine Learning Engineering
• Machine Learning Model Retraining
• Model-Driven Decisioning
• Predictive Modeling
• Exploratory Data Analysis
• Bandit Algorithms
• TensorFlow
• Model Deployment
• Collaboration
• Kubeflow
• Hendrix
Start/End: 2026-06-22 to 2026-12-21
Workplace: Sweden
🔍 Dekoder Ogłoszenia
🔴
spearhead and manage live ML workstreams, ensuring operational excellence and exploratory innovation
Oczekuje się samodzielnego prowadzenia projektów ML na produkcji, co może oznaczać dużą odpowiedzialność i presję.
🔴
Expand the ML action space with novel approaches to transaction handling
Może oznaczać pracę nad nowymi, nieprzetestowanymi rozwiązaniami, które niekoniecznie przyniosą natychmiastowe rezultaty.
🔴
Hendrix
Jest to prawdopodobnie wewnętrzny system lub technologia, której znajomość nie jest powszechna i wymagałaby nauki od podstaw.
🟡
model-driven decisioning
Oznacza, że decyzje biznesowe będą opierać się na wynikach modeli ML, co wymaga silnego zrozumienia ich ograniczeń i interpretacji.
🟡
exploratory innovation
Sugeruje, że część pracy będzie polegać na badaniu nowych możliwości i eksperymentowaniu, co może być mniej ukierunkowane na konkretne, natychmiastowe cele.