MS or higher in the following areas: Statistics and Mathematics
At least 3-5 years of professional industry experience, in addition to your academic experience
Outstanding quantitative analytical ability
Able to take less than precise business requirements and translate them into logic problems which you enjoy solving
Independent and creative approach to problem solving
Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions and methodology to both internal and external customers
In-depth knowledge of Statistics/Probability/Machine Learning
General Statistical concepts such as hypothesis testing, estimation, inference
Supervised and unsupervised statistical techniques such as regression (linear / logistic), time series analysis, clustering
Machine Learning foundations such as bias/variance trade-off, regularization, dimension reduction
Real world experience with popular machine Learning algorithms such as Random Forest, Boosting, SVMs
Experience with unstructured text data using NLP methods such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Sentiment Models, Word Embeddings, Text Similarity, Entity extraction is a strong plus
Strong programming experience in Pyton and one of the following: Scala/Java, R
Understanding of algorithm complexity and performance implications
Knowledge of data structures and algorithms
Good knowledge of Knowledge Engineering
Good knowledge of Graph technology, Knowledge Graphs, Graph Data bases and Ontologies
Experience with SQL
Familiarity with R Shiny framework is a plus