Manager Data Analytics

Role Title : Manager - Data Analytics

Location - Mumbai

Role Purpose: The Data Analytics Team Leader/Manager will spearhead the analytics function, translating complex data into actionable insights that drive efficiency, enhance customer satisfaction, and optimize operational performance. This leadership role requires a blend of technical ex-pertise, project management capabilities, and the ability to mentor and guide a team of data analysts to meet strategic business objectives. This is a highly visible, mid-to-senior level role where you will manage the data analytics team, bridge the gap between technical analysts and business stakeholders, and ensure data integrity and accessibility across the organiza-tion. You will translate the inputs provided (customer satisfaction, ad-hoc requests, score-cards, projects, automation) into strategic initiatives that optimize service delivery and oper-ational efficiency.

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.

Champion continuous learning initiatives, ensuring team members complete relevant certification courses and training programs to enhance skills and contribute to professional development goals. Strategic Project Management:

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

Collaborate with cross-functional departments (Operations, Sales, Finance) to identify analytical opportunities and deliver impactful solutions that optimize logistical processes. Operational 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.