Manager Data Analytics

Role Title : Manager - Data Analytics

Location - Mumbai

Role Purpose:

Key Accountability Area

Team Leadership & Development:

Lead, mentor, and manage a high-performing team of data analysts, fostering a culture of data curiosity, accuracy, and professional excellence.

Strategic Project Management:

• Drive all phases of analytic projects: understand business requirements, build data models, analyze results, and oversee successful implementation.

Reporting & Automation:

• Manage the development, preparation, and timely distribution of monthly operational scorecards to key stakeholders, providing clear visibility into performance metrics.

• Design and implement a robust framework for report automation, migrating recurring reports from manual processes to efficient, automated solutions using modern BI tools.

Ad Hoc Analysis & Stakeholder Support:

• Oversee the intake and prioritization of ad hoc data requests from the business and stakeholders, ensuring timely, accurate, and relevant completion of unplanned or urgent analytical needs. Customer Satisfaction & Performance Measurement:

• Implement and manage a Customer Satisfaction (CSAT) measurement program, surveying key internal customers to gauge the effectiveness and impact of the data analytics team's work.

• Utilize feedback from satisfaction scores to continuously improve service delivery and analytical support quality.

Qualification: Master’s degree in quantitative fields such as Statistics, Mathematics, Engineering, Computer Science, or Operations Management.

Work Experience: Minimum of 8-12 years of overall experience in data analytics, business intelligence, or a related field.

Minimum of 3+ years of experience in a leadership/management role, managing a team of data analysts or data scientists.

Domain Expertise (Mandatory): Proven experience within the Logistics, Supply Chain, Express Distribution, or E-commerce sectors in India is essential.

Technical / Functional Competencies

→ Proficiency in SQL and working with large, complex datasets.

→ Strong command of data visualization tools (e.g., Microsoft Power BI, Tableau) for creating intuitive dashboards.

→ Experience with statistical programming languages (Python or R) for advanced analytics and modelling is highly preferred.

1. Data Analysis & Visualization

Excel (Advanced): Pivot tables, Power Query, macros for quick analysis.

BI Tools: Power BI, Tableau, Qlik for dashboards and reporting.

Data Visualization Principles: Ability to present insights clearly for operational decisions.

2. Programming & Scripting

Python: For data cleaning, statistical analysis, predictive modelling.

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn.

R: For statistical modelling and forecasting (optional but useful).

SQL: Strong skills in querying large datasets from ERP/WMS/TMS systems.

3. Predictive & Prescriptive Analytics

Machine Learning: Demand forecasting, route optimization, inventory prediction.

Optimization Techniques: Linear programming, simulation models for supply chain efficiency.

The following skills and experience are considered a plus:

1. Data Engineering & Big Data

ETL Tools: Talend, Informatica, or custom Python scripts for data pipelines.

Big Data Platforms: Hadoop, Spark (for large-scale logistics data).

Cloud Services: AWS (Redshift, S3), Azure (Data Lake), GCP for scalable storage and analytics.

2. Domain-Specific Tools

ERP/WMS/TMS Analytics: SAP, Oracle Transportation Management, Manhattan Associates.

GIS Tools: For route planning and geospatial analysis (ArcGIS, QGIS).

3. Data Governance & Quality

Data Cleaning & Validation: Ensuring accuracy in shipment, inventory, and tracking data.

Knowledge of APIs: For integrating logistics platforms and IoT devices.

Behavioral Competencies

• Exceptional ability to translate complex data findings into clear, concise, and action-able business recommendations for executive leadership.

• Strong project management skills with the ability to manage multiple priorities in a dynamic, deadline-driven environment.

• Deep understanding of operational processes within the logistics/express delivery ecosystem.

• Excellent communication and interpersonal skills, capable of bridging the gap be-tween technical teams and non-technical business stakeholders.

• Problem Solving & Critical Thinking - Analytical mindset to resolve logistics challenges & Data-driven decision-making and scenario planning.

• Adaptability & Innovation - Embracing change, especially in tech-driven environments & Driving innovation through automation and digitalization.

• Customer Focus - Ensuring high service levels and customer satisfaction.

• Integrity & Accountability - Ethical leadership and responsible decision-making & Ownership of KPIs like on-time delivery, cost savings, and inventory accuracy.