Data Analyst and Scientist
A Data Analyst and Scientist gathers, processes, analyzes and interprets data (structured or unstructured), develops statistical and predictive models, visualizes results and provides decision-oriented insights that support an organization’s strategy and operations. The role may also include creating automated analysis processes, building data pipelines, and collaborating with business, engineering, product or management teams within the company.
Salary
The salary of a data analyst/ scientists can vary depending on the level of experience and the conditions of employment.
Working hours
Variable, depending on the employer and project.
Remote work possibility
Yes, at least partly, depending on the employer.
Types of employers
In Romania, a data analyst/ scientist can work in different formats:
People working as Data Analyst/ Data Specialist can pe employed in:
Tech/ software & IT companies
Institutions in fields such as finances, banking, insurance, retail, marketing, health, public sector, research, services, etc.
Start-ups centered on data, AI, predictive analysis, digital products - where data analysis is critical for business decisions.
Big organizations that manage a large quantity of important data - be they public, private, nonprofit, depending on the field.
Responsibilities
Gathering data from different sources (internal databases, files, APIs, external sources) and aggregating relevant data.
Cleaning, preprocessing and validating the data - assuring the quality of the data, managing errors, aggregating, transforming, uniting sets of data.
Exploratory and statistical analysis: identifying patterns, trends, anomalies; interpreting results; using statistical methods and analytical techniques.
Building analytical or predictive models - statistical models, machine learning models (depending on the position), forecasting, segmentation, predictions, time series analysis, etc.
Data visualization and creation of reports/ dashboards/ presentations that communicate insights clearly, relevantly and in a way that is accessible to non-technical stakeholders (management, business, marketing, etc.)
Communicating and presenting conclusions, recommendations, and data drive actions, collaborating with various departments (business, product, marketing, operations, decision-making)
Implementing and maintaining data infrastructure/ pipelines/ analytical tools - if the role also includes components of Data Science and/ or Data Engineering.
Ensuring compliance with data security and privacy standards, according to internal policies and applicable regulations.
Skills
Technical skills
Strong knowledge of numerical and statistical analysis - mathematics, logics, analytical thinking.
Knowledge of databases and interrogating language, especially SQL.
Attention to details, thoroughness - important for the accuracy of the data and the analysis.
Knowledge of instruments for data visualization and reporting (BI tools, dashboards, graphics) - useful for the efficient communication of insights.
For roles which are specifically described as „Data Scientist”: programming knowledge (Python, R, etc.), modeling abilities, machine learning, the development of analytical products, tests and coding documents.
The ability to manage a large set of data, to manipulate and include multiple sources, to create pipelines and to scale analytical solutions.
Soft skills
Clear communication - the ability to explain insights and complex results in a way that is accessible to non-technical stakeholders.
Critical thinking and problem solving skills: identifying relevant questions, interpreting data, proposing solutions and decisions.
Curiosity, the wish to learn continuously and adaptability, given the quick evolution of technology, methodologies and the quantity of data.
Organizing skills, discipline and the ability to manage time - essential for respecting deadlines and managing analytical projects.
Teamwork and collaborative work - often Data Analysts/ Scientists work together in business, product, engineering or management teams, to deliver quality work.
Qualifications
A person who wants to become a data analyst is recommended to:
Usually a relevant university diploma is required - mathematics, statistics, IT, engineering, data science, economy, business informatics, etc.
Alternatively, you can access entry-level positions through apprenticeships/ trainings (data technician / data analyst apprenticeship), if you have relevant abilities - a specific academic profile is not always mandatory.
For advanced roles/ Data Scientist positions/ predictive or machine learning domains, practical experience with tools and languages such as Python or R, SQL, BI tools, as well as statistical and modeling knowledge, is highly useful.
Practical experience (internships, projects, training programs, participation in real analyses) is a clear advantage - many employers value real world experience over theory.
Sources: (1) + (2)
What else you can do
Specialization toward advanced domains: machine learning, artificial intelligence, big data, data engineering, data governance - depending on personal interests and company needs.
Advancement to senior positions: Senior Data Analyst, Data Scientist, Lead/ Principal Data Scientist, Data Science Manager, Data Consultant, Data Arhitect, etc.
Contributing to the definition of best practices, data quality standards, data governance, documentation and pipeline maintenance.
Interdepartmental collaboration: expanding the impact of data analysis by working with business, product, marketing and operations teams - turning data into strategic decisions.
Continuous learning: staying up to date with tools, methodologies, emerging technologies, and participating in conferences, workshops, and specialized courses.
Sources: (2) + (3) + (4)
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