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?
We are looking for a passionate, creative and autodidact data science expert to join our AI lab and help us build our next line of AI powered features for our flagship product
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.
- Complete ownership of your algorithm - from research and prototype to production and ongoing improvements
- Weave AI powered features throughout our product by applying your knowledge and passion to identify hidden potential tucked deep in our huge amounts of data
- Work with our Product and Engineering teams to build smart algorithms to immediately impact how our users interact and experience data
What Should You Have?
- Ph.D or M.Sc in Computer Science, Mathematics or other related field
- 5+ years of hands-on / industry experience developing machine learning models
- Vast experience in Python and / or R
- Practical experience in applying Deep Learning and fundamental machine learning algorithms
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Random Forests, etc.
- Experience with at least one major deep learning framework e.g. Torch, Tensorflow, Keras, TFLearn
Nice to Have
- Experience with BigData technologies such as Hadoop, Hive, Spark, Presto, etc.
- Experience with running ML on cloud environments such as AWS, GCP, Azure
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Experience in large-scale, data-rich environments
- Proficiency in using query languages such as SQL