Consultant at Fidius Advisory
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Data Scientist - Auto/Manufacturing Industry (4-9 yrs)
The experience and future opportunities will include :
- Data mining and data analyses to generate new insights and open new business opportunities
- Designing a data driven / machine learning product from the bottom up. This includes defining the project goals and understanding the customer problem/journey, carving out the necessary KPIs together with stakeholders, constructing a training/validation and test set, pre-processing the data, employing a machine learning model, and bringing it into production via micro services.
- Proposing and steering our short and long term efforts to exploit new data types and sources, both internally and externally.
- Coordinate model implementation, monitor and refine launched products
- Close collaboration with data engineers and analysts to build products and to grow our customer base.
Who are you?
There is no standard data set or well-trained model that would hold instructions on how to become our new Data Scientist, but to be successful in this role, this is the kind of profile we have in mind :
- You have Masters or PhD in Computer Science, Mathematics, Physics or Applied Science stream
- You have at least 4 years experience in building statistical models, manipulating data sets and made significant contributions to several product launches.
- You can utilize all kinds of data sources, such as relational databases via SQL, non-relational databases, and any kind of web interface providing structured and unstructured data in json, xml, or any other common data format.
- Personal must-haves: strong problem-solving skills, analytic mind, achievement orientation, sense of quality, flexible thinking and good communication skills. You are a self-driven team player with entrepreneurial touch and a passion for results.
- Skills in the areas of Deep learning, NLP and neural networks are highly preferred
- You have profound knowledge and experience with Python or any other object-oriented programming language.
- You have a deeper understanding and already applied various unsupervised-learning techniques, such as k-mean, Gaussian mixture models, DBSCAN, self-organized maps, or autoencoders, as well as in standard supervised learning techniques like regression, random forests, naive Bayes. In addition, you have already designed deep learning models in Keras or PyTorch.
- You were able to gain insight into and experience with the following frameworks, standards, and software categories: Spark, Azure, or similar.
- You have experience visualizing and presenting data utilizing Power BI, Tableau, matplotlib, or Plotly.