Loading...
Role and Responsibilities: Support our pre-sales and post-sales client data science work around our IOT products.
Core Qualifications: Candidates must meet ALL of the following
3+ years of industry work experience, developing data mining models which were deployed and used.
Have analysed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked with others who have put their models into production.
The models should have been validated as robust over at least an initial time period.
Programming experience in Python is core using data mining related libraries like Scikit-Learn. Other relevant Python mining libraries include NumPy, SciPy and Pandas.
Data mining algorithm experience in the families of predictive algorithms (regression, neural nets, decision trees) and clustering algorithms (k-means or other).
Good communication and investigation skills, such as talking to clients. Able to adapt to the right technical level for the audience, focus on the vertical domain and support the business decisions (automated or complimenting a job role).
Bonus Qualifications Any of the following extra qualifications will make a candidate more competitive.
Sets expectations, develops project plans and meets expectations.
Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.
Training or experience in Deep Learning, such as Keras, TensorFlow, convolutional neural networks (CNN) or Long Short-Term Memory (LSTM) neural network architectures. Experience in decision trees like XGboost is helpful.
Vertical experience in Internet of Things (IoT) applications Oil and Gas Motors Wind Turbines Elevators Manufacturing Other IoT Mechanical engineering background or training Time series applications, Digital Signal Processing (DSP)
Experience with Complex Event Processing (CEP) or other streaming data as a data source for data mining analysis Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data.
Have developed frameworks for model automation as a prototype for product