You are a full-stack data scientist, an experienced quantitative thinker who wants to develop further as both a data scientist and an engineer. You are skilled at finding the precise mathematical kernels of real-world problems and want to bring that talent to bear on the business questions facing the world’s leading companies. You are excited to apply your existing expertise in fields such as statistics and computer science on BlackSwan’s ELEMENT state-of-the-art infrastructure. You are excited to work at a startup where you will have a chance to expand your scientific and engineering skills to new areas.
Who We Are:
BlackSwanTechnology.ai is a uniquely positioned data science and human Intelligence insights product company. In our primary application, we offer enterprise level AI empowered Business Applications to the data intensive organisations. We are currently building our next generation Enterprise AI Operating system which will be the world’s most advanced most comprehensive product for Digital Transformation. Our advantage is existing unbelievable human assets, science, engineering, and SaaS product capabilities that align very well with the technology needs.
What A Great Candidate Looks Like:
- 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