Data Scientist

About the role

The company is looking for an analytical, innovative and detail-oriented Data Scientist who turns complex datasets into insights that support business decisions and improve performance. The Data Scientist collects, processes, analyzes and interprets data to find trends, solve business problems, build predictive models and grow operations, working with business teams, data engineers, software developers and management.

Key responsibilities

• Collect, clean, process and analyze structured and unstructured data from multiple sources
• Develop statistical models, predictive algorithms and machine learning solutions
• Perform exploratory data analysis and build forecasting models for demand prediction and customer behavior
• Build, test, validate and refine machine learning models; monitor deployed models and recommend improvements
• Create data visualizations, dashboards and reports for technical and non-technical stakeholders
• Collaborate with data engineers on data pipelines and with business units on KPIs and project objectives
• Run customer segmentation, churn analysis, recommendation modeling and A/B testing
• Document methods, assumptions and experiments, and apply data governance, privacy and security practices
• Present findings to management and automate repetitive analytical tasks

Required profile

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Economics or a related field preferred; a master's degree or advanced training is an advantage
• Previous experience in data science, analytics, statistical analysis or machine learning preferred
• Strong critical thinking, analytical reasoning and problem-solving skills
• Good communication, presentation and collaboration skills, with attention to detail and ethical data use
• Ability to manage priorities, meet deadlines and adapt to changing requirements

Required skills

• Python or R for data analysis and model development
• SQL for querying and manipulating database data
• Statistics, probability, hypothesis testing, regression analysis and experimental design
• Familiarity with machine learning algorithms (classification, clustering, regression, ensemble methods)
• Data visualization tools such as Power BI, Tableau, Matplotlib or Seaborn
• Cloud computing, big data and data warehousing knowledge is an advantage