Principal Data Scientist

**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 as a Subject Matter Expert (SME) in Machine Learning Operation (MLOPs) and AI (Artificial Intelligence) as a Data Science Squad team leader and line manager.

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:

- Data discovery.
- Experimentation with new concepts and building Proof of Concepts (PoCs).
- Iteration over research PoCs to optimize them to deploy them to production.
- Continuous improvement to Syncron’s AI/ML monitoring and tracing capabilities to provide highly reliable and available services to our clients.

**What would you do?**:

- Lead and mentor a team of Data Scientists on the best practices of MLOps.
- Work in cross-functional teams to research, build and deliver the most efficient AI/ML powered solutions for our client problems including 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).
- Enhance and extend the ML (Machine Learning) pipeline to fit an expanding array of Syncron use cases.
- Analyse algorithmic performance of Syncron AI/ML-powered solutions and identify potential issues and provide improvements.
- Provide scientific and technical guidance to other Data Scientists.

**Who you are?**:

- You are KPI (Key Performance Indicators), customer, and Data centric.
- Understanding standard Machine Learning algorithms (like decision trees, neural networks, clustering, and support vector machines).
- You deeply care about your team while leading with empathy and integrity.
- You are a hands-on highly accomplished Data Scientist who has good knowledge and experience of Python.
- You are regarded as a Subject Matter Expert (SME) with hand-on knowledge and experience in building Generative AI-powered knowledge-based services at scale exploring multiple sources of structured and unstructured data using SQL.
- You are an SME in AI/ML-powered fraud detection who has hand-on experience in building fraud prevention models at scale in production using both structured and unstructured visual data.
- Knowledge graph
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- You are enthusiastic about ML technologies.
- Technically curious about the emerging AI innovations.
- Text analytics, LLM and NLP
- Knowledge of Stochastic optimisation models.
- 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.
- 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.

**Tech Stacks**:

- Backend: Python, Fast API
- ML: Kubeflow, MLFlow, Kubernetes Serving.
- Planet Scale Infrastructure: Kubernetes in AWS, Other AWS services like S3, Redshift, Athena, Glue, Spark, EMR
- Development Ecosystem: VSCode, PyCharm, Docker, GitHub, GitHub Actions
- Model Deployment Platform - Angular, Fast API.

LI-SYNCRON
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