As a Data Translator, you will play a pivotal role in bridging the gap between technical data analytics and business stakeholders. You will be responsible for interpreting, translating, and communicating complex data insights in a clear and actionable manner to non-technical audiences. By facilitating effective communication between data scientists, analysts, and decision-makers, you will empower organizations to make informed decisions based on data-driven insights.

REQUIREMENTS

• Master’s degree in a relevant field such as Data Science, Statistics, Business Analytics, or a Business/economics with a focus on Data.
• Proven experience in translating complex technical concepts and data analyses into understandable insights for non-technical audiences.
• Strong analytical skills with the ability to interpret and derive meaningful insights from large and complex datasets.
• Excellent communication and interpersonal skills, with the ability to effectively convey technical information to diverse audiences.
• Proficiency in data visualization tools such as Power BI, Tableau or similar tools.
• Familiarity with programming languages commonly used in data analysis (e.g., SQL, Python, R) is a plus.
• Ability to work collaboratively in a cross-functional team environment and manage multiple priorities in a fast-paced setting.

• Data Interpretation: Translate technical data analyses into understandable insights and actionable recommendations for business stakeholders.
• Communication Facilitation: Act as a liaison between data scientists, analysts, and business stakeholders, ensuring clear and effective communication of data-related concepts and findings.
• Requirement Gathering: Collaborate with business stakeholders to understand their data needs and translate these requirements into actionable tasks for data analysts and scientists.
• Report Generation: Create visually appealing and informative reports, dashboards, and presentations that effectively communicate data insights to diverse audiences.
• Training and Education: Conduct training sessions and workshops to enhance data literacy among non-technical stakeholders, enabling them to better understand and utilize data-driven insights in their decision-making processes.
• Quality Assurance: Ensure the accuracy, relevance, and reliability of data analyses and insights by conducting thorough quality assurance checks.
• Continuous Improvement: Stay updated on emerging trends, tools, and best practices in data analysis and communication to continually improve data translation processes and outcomes.