For our business partner, one of the largest integrators of IT and physical security solutions for national and European critical infrastructure, we are looking for a motivated Data Scientist to analyze large amounts of raw information to find patterns that will help improve business decision-making. Responsibilities:
Data collection and processing: extract and process data from various sources (APIs, databases, files) using Python, R, or other similar languages.
Exploratory data analysis: identifies hidden trends and patterns in complex data sets, performing a detailed exploratory analysis.
Predictive Modeling: develop, validate, and optimize predictive and statistical models for solving business problems using machine learning techniques.
Data presentation and visualization: create attractive and easy-to-understand visualizations with the help of tools like Tableau, Power BI, Matplotlib, Seaborn or ggplot2 to communicate analytical results to non-technical teams.
Process optimization: automate data collection and analysis workflows,
implementing efficient and scalable solutions.
Cross-functional collaboration: works closely with engineering, marketing, sales teams, and other departments to understand requirements and provide data-driven solutions.
Monitoring and reporting: Provide regular reports and analyses to support the decisions strategic of the company.
Requirements:
5+ years of professional experience as a Data Scientist or Data Analyst.
Proficiency in Python or R for analysis and modeling.
Experience with data processing tools (Pandas, Numpy in Python; dplyr, tidyverse in R).
Knowledge of working with relational databases and No SQL (SQL, Mongo DB, etc.).
Familiarity with machine learning techniques and associated packages (Scikit learn, Tensor Flow).
Experience with visualization tools (Tableau, Power BI, Matplotlib, Seaborn, ggplot2).
Proficiency in using data mining tools and APIs.
Analytical mind and business acumen
Problem-solving aptitude.
Excellent communication and presentation skills.
BSc/BA in Computer Science, Engineering, or relevant field.