Job Description

Syncron is a leading SaaS company with over 20 years of experience, specializing in aftermarket solutions. Our Connected Service Experience (CSX) platform offers domain-fit solutions for:

• Supply Chain optimization,
• Pricing strategy,
• Service Lifecycle Management (e.g. warranty management, field service management, service parts management, knowledge management).


Our company has a global presence with offices in US, UK, Germany, France, Italy, Japan, Poland, India and group headquarters in Sweden.


We build upon the belief that our greatest strength is our People. Our unique company culture has been appreciated by our Employees.


With this we are winning the hearts and minds of world-leading organizations , such as JCB, Kubota, Electrolux, Toyota, Renault and Hitachi.







### About the role


You will join a team of talented and friendly Data Scientists in Machine Learning Operation (MLOPs) and AI (Artificial Intelligence) as a Data Science Squad member.


Team develops state-of-the-art Machine Learning-powered services for automated Supply Chain optimisation, Pricing strategy improvements, and Service Lifecycle Management including Generative AI-powered Knowledge management and warranty claim fraud detection and more.


Team is performing full MLOps cycles from customer pain discovery to production including:

### What would you do?

• Work in cross-functional teams to research, build and deliver the most efficient AI/ML powered solutions for our client problems including Knowledge base, Supply Chain optimization and Fraud Detection.
• Work closely with researchers on all the stages MLOps including DS-Ops (Research and spikes), Data-Ops (Data discovery, Feature selection and engineering) and Model-Ops (build, test and ship production-grade models).
• Research design, implement, and deploy machine learning models and algorithms that address specific challenge within after-market, supply chain and price domains.
• Collaborate with team-members and clients globally to understand project requirements, objectives, and constraints.
• Process and analyse datasets to extract meaningful insights and features for model development.
• Design, implement and maintain industry-standard MLOps infrastructure for new and existing ML products
• Optimize and standardize ML training and validation processes, data warehousing and pipelines.
• Add automation, drift detection, logging, version control and testing pipelines to the MLOps architecture.

### Who you are?

• 4 to 6 years of relevant experience in machine learning and AI
• Understanding standard Machine Learning algorithms (like decision trees, neural networks, clustering, and support vector machines).
• You have good knowledge and experience of Python.
• You have deep experience with scientific libraries and frameworks such as NumPy, TensorFlow, Kubeflow, Keras, Scikit Learn, OpenCV.
• Practical industry experience deploying and maintaining ML systems in production.
• Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Experience in building and deploying MLOps pipelines and solutions and working within CI/CD frameworks.
• Experience with Linux systems and cloud infrastructure (AWS, etc.)
• Knows and uses security standards when deploying configurable ML models to production
• Technically curious about the emerging AI innovations.
• Capable of testing using frameworks such as PyTest.

### The icing on the cake:

• Ideally an MSc or PhD in either one of Artificial Intelligence, Computer Science or Applied Mathematics.
• Experience working on Gen AI, LLM, knowledge base and supply chain related projects
• Knowledge of Kubernetes and Docker.
• Familiarity with different image and video data format.
• Familiar with designing, building and troubleshooting distributed and scalable systems.
• Experience in building and maintaining cloud-hosted services on AWS.
• Experience working with MLOps users and data scientists to analyse data,
• Ability to slice and dice big data with SQL using python.
• Experience working with Dag execution workflow engines like airflow, Kubeflow, etc.
• Experience with big data tools: Spark, EMR.
• Has done feature engineering with Python scientific libraries,
• Experience in running machine learning models in AWS cloud.
• Knows about model drift and data drift and how does it affect inferences.
• Managed to deliver scalable inferences.
• Understands platform thinking perspective for delivering machine learning use cases for product teams.

### We offer:

Unsure if you meet all the job requirements but passionate about the role? Apply anyway! Syncron values diversity and welcomes all Candidates, even those with non-traditional backgrounds. We believe in transferable skills and a shared passion for success!



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