Senior Data Scientist at SiSense
We are Sisense; a radically innovative BI company focused on redefining every aspect of business analytics. We love innovation; we always seek to better our solutions and delight our customers. Turning complexity into simplicity is our goal, and we take no less than WOW. Sisense provides a single-stack BI solution, from a blazing fast analytical server that can mash up complex data sets out of various source providers, through a killer analytical product that turns data into actionable insights using proprietary technologies that leave other analytical engines in the dust.
What Are We Looking For?
You will be joining the innovation group called SisenseX, our mission is to predict how people will consume data in the next 5 years, experimenting with cutting-edge technologies to achieve that. Working directly with Sisense Co-Founder and head of innovation, Adi Azaria.
We are looking for a Data Scientist that will help us discover the information hidden in vast amounts of data and help us make smarter decisions to deliver even better products.
What You’ll Do?
Your primary focus will be in applying machine and deep learning techniques and building high-quality prediction systems integrated with our products.
Building smart algorithms to simplify the future of data consumption
Selecting features, building and optimizing classifiers using machine learning techniques
Deep Learning using state-of-the-art methods
Creating large training sets and testing them
Doing ad-hoc analysis and presenting results in a clear manner
What Should You Have?
Advanced degree in a related quantitative field
Practical experience in applying Deep Learning and fundamental machine learning algorithms
(supervised, semi-supervised, and unsupervised learning)
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Random Forests, etc.
Experience with common data science toolkits, such as R, Python. Excellence in at least one of these is highly desirable
Experience with at least one major deep learning framework (such as Caffe, Theano, Torch, Tensorflow, Keras).
Experience with data visualization tools, such as D3.js, GGplot, etc.
Proficiency in using query languages such as SQL
Experience with NoSQL databases, such as MongoDB, Hadoop
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Good scripting and programming skills
Experience with GPU programming
Experience in large-scale, data-rich environments
Great communication skills