Data Collection & Wrangling: Gathering data from various sources (databases, APIs, web scraping, etc.), cleaning it, and transforming it into a usable format. This often involves dealing with missing values, inconsistencies, and different data types.
Exploratory Data Analysis (EDA): Uncovering patterns, trends, and relationships in data through summary statistics, visualizations, and data mining techniques. This helps to form hypotheses and identify areas for further investigation.
Statistical Analysis: Applying statistical methods to test hypotheses, make predictions, and draw inferences from data. This can involve techniques like regression analysis, hypothesis testing, and time series analysis.
Data Visualization: Creating meaningful charts, graphs, and dashboards to communicate insights effectively to stakeholders. This involves choosing the right visualization techniques to tell a compelling story with the data.
Reporting & Communication: Presenting findings and recommendations to both technical and non-technical audiences through reports, presentations, and dashboards. Strong communication skills are essential to convey complex information clearly.
Predictive Modelling: Building models to forecast future outcomes based on historical data. This can involve machine learning techniques like regression, classification, and clustering.
Data Management: Contributing to the development and maintenance of databases and data pipelines. This ensures data quality and accessibility for analysis.
Collaboration: Working with cross-functional teams (e.g., marketing, product, finance) to understand their data needs and provide data-driven solutions.
You Bring
Preferable min. 2-3 years of working experience, preferably in the SI environment.
Diploma or Bachelor's degree in a quantitative field like statistics, mathematics, computer science, economics, or a related field is typically required.
Proficiency in at least one data analysis language like Python (with libraries like Pandas, NumPy, Scikit-learn) or R.
SQL for querying databases, and potentially experience with tools like Excel or data manipulation libraries.
Experience with tools like Tableau, Power BI, Qlik Sense, or data visualization libraries in Python (e.g., Matplotlib, Seaborn).
Familiarity with statistical software packages like SPSS or SAS can be beneficial.
Knowledge of Hadoop, Spark, or other big data technologies may be required for roles dealing with large datasets.
Strong analytical and problem-solving skills are crucial for interpreting complex data and drawing meaningful insights.
Excellent written and verbal communication skills are necessary to present findings clearly and concisely to both technical and non-technical audiences.
Depending on the industry, having domain-specific knowledge (e.g., IT and finance) can be a significant advantage.
