Requesting a Data analyst in a strategic and innovative project to leverage data insights to inform decision-making, drive innovation, and contribute to the project's success. The data analyst should combine technical expertise, analytical skills, and strategic thinking to unlock the value of data and facilitate data-driven decision-making at various levels of the project and organization.
The scope:
Data Collection and Preparation: responsible for collecting, cleaning, and preparing data for analysis. Ensure data quality, integrity, and consistency by identifying and resolving data issues, handling missing values, and transforming data into suitable formats for analysis.
Data Exploration and Analysis: Exploring and analyzing relevant data to identify patterns, trends, and gain a comprehensive understanding of the project's data landscape.
Insights and Recommendations: provide actionable insights and recommendations to project stakeholders. Communicate findings in a clear and understandable manner, using visualizations, reports, and presentations to convey complex information effectively.
Strategic Planning: Collaborate with project stakeholders, including managers, executives, and subject matter experts, to align data analysis goals with the project's strategic objectives.
Performance Monitoring and Evaluation: Establish monitoring systems to track project performance against predefined KPIs. Continuously evaluate project outcomes, identify areas for improvement, and provide ongoing feedback to project teams and stakeholders.
Collaboration and Cross-functional Engagement: Collaborate with cross-functional teams, including data engineers, product managers, domain experts, etc. They work together to integrate data analysis into the project's overall strategy, ensuring alignment with business objectives and driving innovation.
Desired knowledge, experience, competence, skills:
Data Analysis and Statistical Skills: Strong proficiency in data analysis techniques, statistical methods, and quantitative analysis is crucial. This includes knowledge of data exploration, and hypothesis testing.
Data Manipulation and Programming: Proficiency in data manipulation and programming languages such as SQL.
Data Visualization and Communication: Proficiency in data visualization tools (Power BI) and the ability to effectively communicate complex findings to both technical and non-technical stakeholders is critical. Strong presentation skills, storytelling, and data visualization techniques help in conveying insights in a clear and compelling manner.
Problem-solving and Critical Thinking: Should possess strong problem-solving and critical thinking skills. They need to identify key challenges, formulate hypotheses, design appropriate analytical approaches, and evaluate alternative solutions. Being able to think analytically and logically is essential for deriving insights from data.
Project Management: Demonstrated ability to manage projects effectively, prioritize tasks, and meet deadlines is important.
Autonomy: Should be able to work independently as part of a team.
What 3 most important: