Data Science Lead

About The Role

Main Responsibilities:

1. Manage the delivery of data science solutions from inception, through Proof of Concept and into production, using a range of tools and methods.
2. Data Analysis: Collect and analyze data from various sources, including internal systems, third-party tools, and market research.
3. Dashboard Implementation and Creation: Oversee the development, implementation, and maintenance of dynamic dashboards to visualize key metrics, enabling stakeholders to monitor performance and make data-driven decisions.
4. Key Performance Indicators (KPIs): Define KPIs and develop reports to monitor and measure performance based on these indicators.
5. Cross-Department Collaboration: Work alongside IT teams to ensure that data infrastructure, systems, and tools are optimized for efficient collection, storage, and analysis.
6. Data Structure and Sources: Play a crucial role in deciding the data structure and determining the most relevant and reliable data sources for business analysis. Ensure data integrity and consistency across all platforms.
7. Data Visualization: Create compelling presentations that convey analytical insights and actionable recommendations to senior management and stakeholders.
8. Data-Driven Decision Culture: Foster an organizational culture that values and utilizes data-driven decision-making, promoting continuous improvement and innovation.
9. Make business recommendations based on the data that drives better business decisions.

Requirements

1. Education: Bachelor’s degree in Mathematics, Statistics, Computer Science, or a related field.
2. Experience: Over 3 years of experience in data analytics.
3. Technical Skills: Proficiency in data analysis and visualization tools such as SQL, Excel, Tableau, Power BI, or similar, with a focus on Power BI experience.
4. Dashboard Creation Expertise: Extensive experience in designing and implementing effective dashboards that provide real-time insights and enable the business to track performance and make informed decisions.
5. Additional Knowledge: Experience in strategic planning using quantitative techniques, managing large data volumes, and advanced statistical techniques.
6. Interpersonal Skills: Excellent communication and presentation skills, with the ability to translate complex data into clear, actionable insights.
7. Experience using Data Science algorithms and methods to solve business problems using R or Python.
8. Excellent capability in SQL to extract data from databases.