Data Analyst

Job Details

• Data Consolidation & Cleaning to make it consumable for further deep dive analytics
• Data analytics & interpretation aligning to day-to-day deliverables in the production
• Data Visualization & Reporting by generating dashboards that triggers action from internal customers
• Trend Analysis & Forecasting - Use historical data to Analyze trends and forecast future patterns.
• Database Management - Manage and query databases (e.g., SQL, NoSQL) to retrieve and analyze data
• Sale Performance Analytics - Sell thru / Sale age / Sale locations of different product groups/ new products.
• Collaboration with Digital team to collect data / generate reports aligning to the business requirement

Job Requirements

Education

Bachelors degree in data science, Statistics, Mathematics, Computer Science, or a related field (or equivalent experience).

Relevant Experience

• 3+ years of experience working as a Data Analyst
• Experience working with large datasets and performing complex data analysis

Behavioural Skills

• Drive for Results
• Influence for Impact
• Deals with Ambiguity
• Interpersonal Effectiveness
• Nurtures Relationships
• Business Acumen

Knowledge

• MS Excel - Advanced
• Proficiency in Data analytics software - Python / R Programming
• Proficiency in Data Visualization Software - Preferably Power BI

Process Contribution

Process Outcome

Performance Measure

1. Reduction in Manhours Spent in generating Reports and dashboard in Production planning

• Improved Accuracy in Planning
• Reduction in NVAs @ Planning
• Effective utilization of Resources

No. of reports automated | Reduction in % of Errors in Data reporting

2. Forecast Accuracy in Production planning at SKU level to build enough capacity and capability esp. during peak seasons

• Improved Capacity utilization
• Improved Delivery performance
• Control over Mfg. Cost

Capacity Utilization 85% -100% | Forecast accuracy @ SKU level - 90%+

3. On-time Declaration of Production performance Dashboards to enable effective reviews with Stakeholders

• Data Driven decision making
• Accountability to relevant stakeholders
• Escalation of deviations on time

Availability of Data at Designated Timeline ( Review Schedule )

4. Dynamic & Real-time Data analytics to drive efficiency in Production Flow

• Identify Non-Moving Orders to act
• Predicting Order failures in advance
• Dynamic Order Planning & Scheduling

Hourly Data update (Key Metrics) | % Order failures avoided | Alignment % of Order Scheduling at each process Stage

5. Sale Performance Dashboard against Key Collections/ New Designs

• Identify Best Sellers in a group
• Provide input to NPD
• CIM Level Performance declaration

Weekly Declaration of Sell thru. Performance