Min. Qualification:

Candidates must meet ALL of the following qualifications:

Have analyzed, 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.

Three years of industry work experience, developing data mining models which were deployed and used.

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 at least 3 algorithms across: prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other) or Bayesian networks

Bonus Qualifications

Any of the following extra qualifications will make a candidate more competitive:

Soft Skills

• 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.
• Must be able to work with stakeholders in different timezones.

Technical skills

• Commonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.
• 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.
• Training or experience in Deep Learning, such as TensorFlow, Keras, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures. If you don’t have deep learning experience, we will train you on the job.
• Shrinking deep learning models, optimizing to speed up execution time of scoring or inference.
• OpenCV or other image processing tools or libraries
• Cloud computing: Google Cloud, Amazon AWS or Microsoft Azure. We have integration with Google Cloud and are working on other integrations.
• Decision trees like XGBoost or Random Forests is helpful.
• Complex Event Processing (CEP) or other streaming data as a data source for data mining analysis
• Time series algorithms from ARIMA to LSTM to Digital Signal Processing (DSP).
• Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN)
• Experience with PMML is of interest (see www.DMG.org).

Vertical experience in Industrial Internet of Things (IoT) applications:

Energy: Oil and Gas, Wind Turbines

Manufacturing: Motors, chemical processes, tools, automotive

Smart Cities: Elevators, cameras on population or cars, power grid

Transportation: Cars, truck fleets, trains

Skills Required:

Prediction Analytics, Neural Networks, Decision Trees, Clustering, Scikit-Learn

Roles:

• Execute data mining projects, training and deploying models over a typical duration of 2 -12 months.
• The ideal candidate should be able to innovate, analyze the customer requirement, develop a solution in the time box of the project plan, execute and deploy the solution.
• Integrate the data mining projects embedded data mining applications in the FogHorn platform (on Docker or Android).