Dois conceitos bem interessantes trazidos pelo Vincent Grandville que mostram o porque que os recrutadores de TI/Análise de Dados devem ter atenção no momento de análise de candidatos:
- Vertical data scientists have very deep knowledge in some narrow field. They might be computer scientists very familiar with computational complexity of all sorting algorithms. Or a statistician who knows everything about eigenvalues, singular value decomposition and its numerical stability, and asymptotic convergence of maximum pseudo-likelihood estimators. Or a software engineer with years of experience writing Python code (including graphic libraries) applied to API development and web crawling technology. Or a database guy with strong data modeling, data warehousing, graph databases, Hadoop and NoSQL expertise. Or a predictive modeler expert in Bayesian networks, SAS and SVM.
- Horizontal data scientists are a blend of business analysts, statisticians, computer scientists and domain experts. They combine vision with technical knowledge. They might not be expert in eigenvalues, generalized linear models and other semi-obsolete statistical techniques, but they know about more modern, data-driven techniques applicable to unstructured, streaming, and big data, such as (for example) the very simple and applied Analyticbridge theorem to build confidence intervals. They can design robust, efficient, simple, replicable and scalable code and algorithms.