Data Scientist in Bucuresti

Job Description Responsibilities Selecting features, building and optimizing classifiers using machine learning techniques Data mining using state-of-the-art methods Extending company’s data with third party sources of information when needed Enhancing data collection procedures to include information that is relevant for building analytic systems Processing, cleansing, and verifying the integrity of data used for analysis Doing ad-hoc analysis and presenting results in a clear manner Creating automated anomaly detection systems and constant tracking of its performance Skills and Qualifications Programming Skills – knowledge of statistical programming languages like and database query languages like is desirable. Familiarity with Scala or is an added advantage Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies – good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, XGB, ANN, RNN, NLP Strong Math Skills (Multivariable Calculus and Linear Algebra) - understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of a lot of predictive performance or algorithm optimization techniques Data Wrangling – proficiency in handling imperfections in data is an important aspect of a data scientist job description Experience with like matplotlib, ggplot, , Tableau that help to visually encode data Explaining and presenting analytical results Agile development methodology Statistical approaches and methods and their application on projects Data mapping, meaning of relational data and object orientation Understands projects needs and can translate these into detection models Model documentation, ideally AML model governance Analytical mind and great business sense Excellent Communication Skills – it is incredibly important to describe findings to a technical and non-technical audience Problem-solving aptitude Nice to have Exposure to risk, fraud, financial crime, customer insight or compliance-based environments that utilise detection / predictive models BSc/BA in Statistics, Mathematics, Computer Science or another quantitative field. Software Engineering Background Experience with NoSQL databases, such as Elasticsearch