Sia
9 months ago
Consultant in Data Science
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Company information
- Company
- Sia
- Location
- België, Antwerpen (Provincie), Antwerpen Belgium
- Posted
- 9 months ago
Job description
Job Description
As a consultant in Data Science, you will be involved in transformation projects at our clients. You will execute project assignments in modelling and digital transformation (business/IT analysis and partial coding) environments with a good deal of business perspectives:
- Machine learning, AI and statistics: developing predictive models of decision making (supervised and unsupervised learning algorithms, econometrics, forecasting, quantitative diagnosis,...)
- Operational research : assistance in the design and implementation of constraint optimization solutions and simulation models
- Big Data : Algorithmic knowledge for large and unstructured data processing
You will get the opportunity to actively contribute to Sia’s internal activities:
- Development and reinforcement of our offer, by understanding and interpreting data science innovation and challenges
- Business development, by contributing to need definition and participating in our commercial initiatives
Equipped with the entrepreneurial fibre, you are ready to cooperate in a fast growing company.
In return, you can expect:
- An international company that gives importance to diversity, inclusion, employee well-being and corporate social responsibilities actions.
- A great working atmosphere with friendly and professional colleagues where innovation and entrepreneurship are encouraged
- A personal development plan with a regular follow-up as well training opportunities to develop yourself
- A competitive salary with great advantages, like mobility budget, hospital insurance, performance -related bonus,...
Required skills
- ai
- big data
- machine learning
- forecasting
- statistics
- entrepreneurship
- predictive models
- econometrics
- quantitative diagnosis
- operational research
- simulation models
- algorithmic knowledge
- constraint optimization solutions
- challenges
- supervised learning algorithms
- unsupervised learning algorithms
- data science innovation
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