Senior Data Scientist
2 semanas atrás
Horário:
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Senior Data Scientist - Digital Enterprise Transformation
Do you want to push the boundaries of machine learning and scale it to users across the world? Do you wish for a product centric work environment, where machine learning and analytics drive action - not just insight?
With a strong focus on the MLOps discipline you will take part in the innovation and design of new products, as well as tweak and mature existing ones.
- and frontend developers and DevSecOps specialists. You will be responsible for a product agnostic guild of data scientists and ML engineers, aiming to create and continuously enhance the ML framework of tomorrow.
Join a department focused on our most valuable digital products
You will join the Digital Enterprise Transformation department.
The purpose of our department is to build advanced end-to-end products which create direct business value for DSV's divisions, including:
- Customs declaration automation
- Vendor invoices automation
- Booking distribution
- Address validation
- ETA prediction
The use cases we solve tend to have a high degree of complexity, requiring non-deterministic problem solving (i.e., the use of ML/AI), near real-time data processing, a need for high availability, vertical and horizontal scalability, and a very high volume of transactions.
Our new unit is characterized by having a startup mindset, and it is divided into cross-functional product teams with a mix of career starters and highly experienced colleagues.
We strive to base our work on knowledge and insight rather than hierarchical structures, and we make sure that our decisions are based on conversations between people with different competencies rather than one individual.
Be the driver of our AI/ML and analytics journey
As our next Senior Data Scientist, you will use the right tools in the toolbox to solve the issues at hand.
- Meeting with stakeholders, discussing their needs and translating them into Data Science problems.
- Conduct datadriven analyses to provide input to decisions (e.g. which DSV country to start with for a specific use case).
- Identifying proper solutions / models / pretrained models for particular business cases.
- Preprocessing the data (cleaning, transforming) for ML models.
- Train ML models on prepared training data using GPU on cloud and on premises.
- Test and evaluate ML models and present conclusions on further improvements.
- Using and tweaking existing frameworks for running automated testing and evaluation of ML models.
- Maturing models to be available as services for other components.
- Present your results on an ongoing basis to team mates and business stakeholders.
You thrive when solving high-complexity challenges
Solving problems too complex for deterministic reasoning with the use of machine learning, you need to be able to thoroughly analyze problems using data and statistics.
With these skills, you make realistic mockups of data to allow you to test things swiftly, finding pragmatic solutions that balance between the theoretical standpoint and what is possible.
Additionally, we expect you to have experience with most of the following technologies/areas:
Code languages: Python (using both OOP as well as popular libraries such as Pandas, Scikit),
Solid understanding of Machine Learning in practice: be able to speak about several projects you participated in,
-
Very good knowledge of Deep Learning with focus on
NLP:
Transformers, Bert family and where it all came from,
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MLOps frameworks:Google Vertex AI & Kubeflow are the core of our MLOps platform. Furthermore, we expect that you have substantial experience with other MLOps (AWS, Azure, other) solutions and you can challenge our ways of working.
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Cloud architecture:Azure
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ML Frameworks:PyTorch & TensorFlow
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ML model serving:TorchServe & TensorFlow Serving
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Coding languages:Python & Java
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Database technologies such as:MySQL & MongoDB
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Version control:Git, DVC, Azure DevOps
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Containerization and orchestration: Docker & Kubernetes
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Modern software development: good understanding of DevOps, CI/CD, MLOps,
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Basic knowledge of database technologies such as:
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Relational database:We use MySQL
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NoSQL database:We use MongoDB
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Version control:Git (we use Atlassian BitBucket as a GUI on top of Git)
We also work with other technologies that you might not be an expert in such as:
Event streaming:Confluent Kafka, K streams
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