Data Analytics & Optimisation Engineer

Data Analysis and Insights Generation:

• Collect, clean, and preprocess large and complex datasets from multiple sources. This may include structured data from databases and unstructured data from logs, documents, and other relevant sources.
• Apply advanced statistical and analytical methods to explore the data. Conduct descriptive, diagnostic, predictive, and prescriptive analyses to uncover hidden patterns, trends, and relationships within the data.
• Interpret and visualize data to communicate findings effectively to both technical and non - technical stakeholders. Develop interactive dashboards in Power BI, as well as reports, and visualizations using data visualization tools to present complex data in an understandable manner. 

Optimization Modelling:

• Identify optimization opportunities within business processes, systems, and operations by using data analysis and modelling tools.
• Develop mathematical models and algorithms to represent optimization problems. Assist in the utilization of techniques such as linear programming, integer programming, nonlinear programming, or simulation to find optimal solutions.
• Collaborate with subject matter experts to understand the business context and constraints of optimization problems to ensure the developed models are practical.

Data-Driven Decision Support:

• Provide data driven recommendations and insights to support strategic decision making at various levels.
• Conduct ad - hoc data analysis to answer specific business questions and support decision making in projects or initiatives.
• Collaborate closely with business leaders to understand their needs and provide timely and relevant data analysis.

Communication and Coordination:

• Work closely with cross functional teams to collaborate on projects, share insights, and ensure that data analytics and optimization efforts are integrated with overall business strategies.
• Participate in meetings, workshops, and brainstorming sessions. Communicate technical concepts to non - technical stakeholders effectively and translate business requirements into data driven optimization projects.

1. Master's degree in engineering, Mathematics, Statistics, Computer Science, or a related field. A degree with a focus on data analytics, operations research, or optimization is highly desirable.

2. Be familiar with data analysis, preparation and transformation using programming languages such as Python, R or Julia. 

3. Experience with data analysis libraries and data visualization tools (e.g., Tableau, PowerBI, Matplotlib).

4. Basic knowledge of statistical analysis and machine learning techniques (regression analysis, clustering, classification, and time - series analysis). 

5. Experience with predictive analytics. 

6. Familiarity with data infrastructure and database systems, such as SQL databases (e.g., MS SQL Server) and big data platforms (e.g. Spark, Snowflake, Microsoft Synapse Analytics). 

7. Basic knowledge of data warehousing concepts. 

8. Good English skill (B2, IELTS 6.5). Good German skill is preferred. 

9. Strong problem-solving skills, with the ability to think critically and logically to solve complex data related problems. 

10. Strong attention to details and a commitment to data quality and accuracy. 

11. Curiosity and a passion for exploring data to discover new insights and opportunities for improvement. 

12. Great communication and interpersonal skills. Great presentation skills to explain data analysis results to the stakeholders especially to non-technical background clients. 

13. Ability to work effectively in a team environment and collaborate with colleagues from different backgrounds.