Manager AI/ML

Syncron is a leading SaaS company with over 20 years of experience, specializing in aftermarket solutions. Their Service Lifecycle Management Platform offers domain-fit solutions for Supply Chain optimization, Pricing strategy, and Service Fulfillment (e.g. warranty management, field service management, service parts management, knowledge management). The company has a global presence with offices in US, UK, Germany, France, Italy, Japan, Poland, India and group headquarters in Sweden. They serve world-leading organizations such as JCB, Kubota, Electrolux, Toyota, Renault and Hitachi.

• Advanced degree in Data Science, Machine Learning, Computer Science, Statistics, or related field; PhD preferred.
• Minimum 12 years of experience in data science and machine learning, with at least 3 years in a leadership role managing teams.
• Proven ability to lead and collaborate with distributed and remote teams.
• Experience deploying ML models in production environments at scale.
• Developed scalable ML systems and architectures supporting model reusability across multiple customers with minimal customization.
• Expertise in Python, R, SQL; machine learning frameworks TensorFlow, PyTorch, scikit-learn; MLOps tools MLflow, Kubeflow, SageMaker; and AutoML platforms Google AutoML, H2O.ai, Azure AutoML.
• Knowledge of data engineering, data pipelines, data lake, data lakehouse, and data mesh architecture.
• Proficient in Python for model development and data manipulation.

Nice to have:

• Experience with Spark and Mage AI.
• Experience with LLMs, generative AI including productionalizing chatbots.
• Background in software engineering or DevOps.
• Previous domain experience in Supply Chain, Pricing, Inventory, and Service Fulfilment.

• Develop and execute the organization's data science strategy, focusing on advancing machine learning, MLOps, and AutoML practices to optimize operational workflows and model lifecycle management.
• Lead, mentor, and grow a dynamic team of data scientists, ML engineers, and specialists, fostering a collaborative and innovative work environment.
• Define and evolve the long-term AI/ML architecture roadmap, ensuring scalability, maintainability, and integration with existing systems.
• Define and implement a roadmap for data collection, storage, and analysis, emphasizing integration of machine learning pipelines, scalable MLOps frameworks, and AutoML systems.
• Oversee deployment of predictive models, machine learning algorithms, and statistical analyses, ensuring seamless integration of AutoML platforms.
• Improve model lifecycle time and scalability by implementing cutting-edge practices in MLOps and AutoML to reduce time-to-market and ensure robust solutions.