The discussion about the roles in data science is hot: data is a new oil and companies’ increased focus on acquiring best talent is increasing with the creation of a whole new set of data science roles and titles. To succeed, business owners should be familiar with the terminology, be precise in job offers, manage roles and responsibilities to ensure data deliver value.
The data engineer is someone who develops and maintains architectures, such as databases and large-scale processing systems.
Responsibilities: Extract-Transform-Load (ETL) data, remove corrupted data, optimise data for analysis.
Key tools for data engineering: Neo4j, Redis, Hadoop, Spark, Scala, Cassandra, PostgreSQL, Oracle, MongoDB.
Key background: Computer Engineering, Database Administration (DBA).
Data scientists get data that has passed a first round of cleaning and manipulation, which they can feed to sophisticated analytics programs and machine learning and statistical methods to prepare data for use in predictive and prescriptive modelling.
Responsibilities: Data exploration and visualisation, experimentation and prediction.
Key tools for data science: Tableau, SAS, SPSS, R, Python, RapidMiner, Knime, Matlab, Wolfram Mathematica.
Key background: Statistics, Econometrics, Computer Science.
Domain Expert is someone who has in-depth knowledge of data sources available (internal and external), data pricing, industry insights re predictive models and added value they can generate for the business. Domain Expert helps to setup budget, implement models into business and estimate Return on Investment.
Responsibilities: Data sourcing, Independent Model Quality Assurance, Financial Planning and Analysis, Project Management.
Key background: MBA, Econometrics, Computer Science.