Syncron Overview
Syncron is a top-tier SaaS provider with over 20 years of expertise in aftermarket solutions. Our Connected Service Experience (CSX) platform provides tailored solutions in:
- Supply Chain Optimization
- Pricing Strategy
- Service Lifecycle Management (including warranty, field service, service parts, and knowledge management)
With a global footprint, Syncron has offices in the US, UK, Germany, France, Italy, Japan, Poland, India, and headquarters in Sweden.
Our people-first culture is our strength, winning the trust of world-leading clients like JCB, Kubota, Electrolux, Toyota, Renault, and Hitachi.
About the Role
As a Data Science Squad member, you’ll collaborate with a team of skilled Data Scientists focused on Machine Learning Operations (MLOps) and Artificial Intelligence. Our team develops advanced ML-powered services for:
- Automated Supply Chain Optimization
- Enhanced Pricing Strategies
- Service Lifecycle Management (including Generative AI for Knowledge Management and fraud detection in warranty claims)
The team manages complete MLOps cycles, from customer problem discovery to production deployment.
What You’ll Do
- Collaborate with researchers across all stages of MLOps, including DS-Ops (Research), Data-Ops (Data discovery and feature engineering), and Model-Ops (Building, testing, and deploying models).
- Partner with cross-functional teams to develop AI/ML solutions for knowledge base, supply chain optimization, and fraud detection.
- Research, design, and implement machine learning models tailored to aftermarket, supply chain, and pricing challenges.
- Work closely with team members and global clients to understand project objectives and constraints.
- Process and analyze datasets to generate insights and create features for model development.
- Design and maintain robust MLOps infrastructure for new and existing ML products.
- Standardize and optimize ML training, validation, data warehousing, and pipelines.
- Add automation, drift detection, logging, version control, and testing pipelines to MLOps architecture.
Who You Are
- Knowledgeable in standard ML algorithms (e.g., decision trees, neural networks, clustering).
- Proficient in Python with experience in scientific libraries (NumPy, TensorFlow, Kubeflow, Keras, Scikit Learn, OpenCV).
- Experienced in deploying and maintaining ML systems in production.
- Skilled in data preprocessing, feature engineering, and model evaluation.
- Practical experience in Gen AI, LLM, knowledge base, and supply chain-related projects.
- Competent in deploying MLOps solutions within CI/CD frameworks.
- Experience with Linux systems and cloud infrastructure (e.g., AWS).
- Curious and technically adept, especially with emerging AI innovations.
- Familiar with frameworks like PyTest for testing.
Bonus Points
- Advanced degree (MSc or PhD) in AI, Computer Science, or Applied Mathematics.
- Hands-on experience with Kubernetes, Docker, and AWS-hosted services.
- Knowledge of distributed and scalable system design.
- Proficiency in SQL for big data processing and Python.
- Experience with DAG workflow engines (Airflow, Kubeflow).
- Expertise in big data tools such as Spark and EMR.
- Strong knowledge of model drift and data drift and their impacts on inference accuracy.
- Familiar with platform thinking for delivering ML solutions in product teams.