Data Analyst

Roles and responsibilities

• Manage supply chain data and related analytics by designing, producing, analyzing, continuously improving, and presenting monthly logistics and supply chain metrics, measuring operational and financial performance.
• Design and produce monthly internal reports and metrics to measure performance against targets/budgets, and to measure internal operational performance, identify trends and issues, and to measure internal efficiency
• Identify, prioritize structure, and solve complex supply chain problems using data mining, analytics, and business judgment
• Summarize findings into useful information with implication for increase revenue, cost reduction, and/or driving supply chain business decisions.
• Plan and implement supply chain optimization projects related to inventory management, cost optimization, and route planning, to meet business requirement
• Regularly track KPIs and report on supply chain performance
• Create dashboards, reports, and analysis that explain trends – what happened, what may happen, and why
• Handle reoccurring analysis and tasks aimed at driving continuous cost savings Initiatives
• Examine current processes to identify shortcomings and implement or recommend improvements to drive out waste
• Responsible for planning and documenting standard operating processes
• Advanced MS Excel, Power BI and Python data analytics skill.

Qualifications

• Degree / Diploma in any field, preferably in Supply Chain Management
• 3-5 years relevant work experience, preferably from Aviation sector.
• Proficient in Computer Applications
• Good English communication skills, verbal and written.
• Attention to Details

Desired candidate profile

1. Data Collection and Management

• Data Gathering: Collect data from various sources, including internal systems, third-party providers, databases, and surveys. Ensure data is accurate, up-to-date, and relevant to the business’s needs.
• Data Cleaning: Cleanse data by identifying and correcting errors or inconsistencies in datasets. Remove duplicates, correct formatting errors, and handle missing data to ensure the integrity of the analysis.
• Database Management: Maintain and manage databases by ensuring that the data is properly structured and accessible for analysis. This may involve using database management systems like SQL, MySQL, or other cloud-based platforms.

2. Data Analysis and Interpretation

• Exploratory Data Analysis (EDA): Conduct initial data analysis to identify trends, patterns, and anomalies in the dataset. This could include basic statistical analysis, such as mean, median, mode, and standard deviation, as well as visualizing data distributions using graphs and charts.
• Statistical Analysis: Use statistical methods and tools to analyze data and test hypotheses. This may include regression analysis, correlation analysis, and time series analysis, among other advanced techniques.
• Predictive Analysis: In some roles, data analysts may also work with machine learning algorithms to build predictive models that help forecast trends or outcomes based on historical data.

3. Reporting and Data Visualization

• Creating Reports: Prepare detailed reports and summaries of data findings. Present these findings in a clear and understandable format, often using PowerPoint, Excel, or other reporting tools.
• Data Visualization: Use tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) to create visual representations of data, including charts, graphs, and dashboards that help stakeholders understand complex data at a glance.
• Presenting Insights: Present findings to key stakeholders, including management, marketing teams, or clients. This requires the ability to translate complex data into actionable insights and recommendations.

4. Collaboration with Cross-Functional Teams

• Collaboration with Teams: Work closely with different departments, such as marketing, finance, operations, and product development, to understand their data needs and provide insights that help solve business problems or optimize processes.
• Data Strategy: Participate in developing data strategies and assist other teams with data-driven decision-making. Help set data collection standards and best practices across the organization.
• Feedback Loop: Continuously refine analysis methods based on feedback from business teams. Adjust metrics, KPIs, or data models as needed to align with business goals.

5. Data Integrity and Quality Assurance

• Quality Control: Ensure the data used for analysis is of high quality by regularly validating datasets for completeness, consistency, and reliability.
• Documentation: Document data sources, methodologies, and assumptions made during analysis to ensure transparency and replicability.