Responsible for developing scientific methods, processes, and systems to extract knowledge or insights to drive the future of applied analytics.

Provide thought leadership, perform Advanced Statistical Analytics, and create insights into data to provide to the business actionable insights, identify trends, and measure performance which address business problems.

Collaborate with business and process owners to understand business issues, and with engineers to implement and deploy scalable solutions, where applicable.

Our Exciting Opportunity

Responsible for developing scientific methods, processes, and systems to extract knowledge or insights to drive the future of applied analytics.

Provide thought leadership, perform Statistical Analytics, and build insights into data to deliver to the business useful insights, identify trends, and measure performance which address business problems.Collaborate with business and process owners to understand business issues, and with engineers to implement and deploy scalable solutions, where applicable.

You will:

• Perform Data science leadership

• Synthesize problems into data question(s)

• Decide approach for data science

• Design perform data science experiment

• Develop Data Science Infrastructure Tools

• Convert data into useful insights.

• Act in external relations

To be successful in the role you must have:

Theoretical Knowledge:

• Common and sophisticated Clustering algorithms including probability density and distribution based.

• Convolutional Neural networks

• Recurrent Neural Networks

• Bayesian based probabilistic graphical models

• Deep Auto encoders

• The latest machine reasoning techniques

• Reinforcement learning

Application of ML/AI:

• Clean clustering of high dimensional data with noise

• Application of auto encoders to high-dimensional data

• Application of CNN to high-dimensional temporal data

• Application of RNN to high- time series data for forecasting

• Application of Bayesian dimensional networks for causal reasoning

• Auto learning of network structure from data for probabilistic graphical models

Frameworks, Languages, General skills:

• Strong programming skills in Python, ability to write object oriented and scalable code

• Jupyter Hub and notebooks

• Solid skills in the use of current innovative machine learning frameworks such as Tensorflow, Keras, Scikit-learn, etc.

• Ability to handle large amounts high dimensional data (knowledge of MapReduce or PySpark is a plus)

• Good data Visualization skills

• Ability to work independently and be a self-starter with high energy, passion and persistence