Data Scientist

Develop, exploit, and maintain data products. Data Engineering teams are responsible for the delivery and operational stability of the data products built and provide ongoing support for those products. Data Engineers work within, and contribute to, the overall data development lifecycle process as part of multi-functional Agile delivery teams focused on one or more products. Data Scientists should have the following skills: • Data science foundation - a data scientist must be able to: – Characterize a business problem – Formulate a hypothesis – Demonstrate the use of methodologies in the analytics cycle – Plan for the execution Understanding the data science workflow and recognizing the importance of each element of the process is critical for successful implementations. • Statistics and programming foundation (Analysis &Visualisation) - the competencies in this area are focused on the knowledge of key statistics concepts and methods essential to finding structure in data and making predictions. Programming skills (R/Python) or other statistical programming skills are essential —and the ability to visualize data, extract insights and communicate the insights in a clear and concise manner. • Data preparation - to ensure usable data sets, the key competencies required are: – Identifying and collecting the data required – Manipulating, transforming and cleaning the data A data scientist must deal with data anomalies such as missing values, outliers, unbalanced data and data normalization. • Model building - this stage is the core of the data science execution, where different algorithms are used to train the data and the best algorithm is selected. A data scientist should know: – Multiple modelling techniques – Model validation and selection techniques A data scientist must understand the use of different methodologies to get insight from the data and translate the insight into business value. • Model deployment - an ML model is valuable when it’s integrated into an existing production environment and used to make business decisions. Deploying a validated model and monitoring it to maintain the accuracy of the results is a key skill. • Big data foundation - a data scientist deals with large volume of structured and unstructured data, they must demonstrate understanding of how big data is used, the big data ecosystem and its major components. The data scientist must also demonstrate expertise with big data platforms, such as Hadoop and Spark and master SQL and NoSQL. • Leadership and professional development - data scientists must be good problem solvers. They must understand the opportunity before implementing the solution, work in a rigorous and complete manner, and explain their findings. A data scientist needs to understand the concepts of analysing business risk, making improvements in processes and how systems engineering works. Typically Data Scientist Skills required includes: • Proficient in Python, SQL • Machine Learning frameworks: scikit-learn, Tensorflow or PyTorch • NLP libraries: NLTK, spaCy, openNLP, Stanford-NLP, WordNet