BRIEF OVERVIEW-
Who We Are-
Maruti Suzuki India Limited (MSIL), a subsidiary of Suzuki Motor Corporation, Japan, is India’s largest passenger car maker. India’s first company to produce and sell more than 2 million cars in India in a year, Maruti Suzuki is credited with having ushered in the automobile revolution in the country. The Company, formerly known as Maruti Udyog Limited, was incorporated as a joint venture between the Government of India and Suzuki Motor Corporation, Japan in February 1981. From the day the iconic Maruti 800 was launched in 1983, the company has been spearheading a revolution of change. The Company has grown leaps and bounds, manufacturing close to 2 million cars a year in FY 2022-23 and the Company’s turnover exceeded 1 lakh crore, a feat which is achieved only by a few manufacturing companies in India.
Maruti Suzuki is successfully striving upwards and onwards driven by its values of Customer Obsession, Openness & Learning, Networking & Partnership and Fast, Flexible First Mover.
The Company has embarked on an ambitious path in terms of manufacturing scale, while simultaneously transitioning towards a low-carbon path. This marks the new phase for the Company.
PRINCIPAL ACCOUNTABILITIES
To lead business analytics initiatives in line with the business objectives
Develop business cases and proposals for analytics initiatives
Collaborate with stakeholders to identify business needs and develop analytics solutions
Create and communicate reports through data visualizations and present findings and recommendations to executives and stakeholders
Identify Challenges for different Projects using insights shared by Research & Analytics team and propose potential solutions for business impacts.
Lead and Mentor a team of Analytics professionals
Develop team goals and objectives to meet organizational goals
MAJOR CHALLENGES
Data Quality & Accessibility: Data silos, Data Quality issues, Data privacy and security
Evolving business needs: Rapid changes and Shifting priorities
Technical complexity: Advanced Analytics and Tool Proficiency
Integration with businesses processes: Resistance to change and alignment with strategic objectives
Bias in data and Privacy concerns
DECISIONS
Decisions by Analyst
: Data Analysis and Visualization, Ad-hoc analysis, dashboard design, data quality & governance, Tool selection, Team management
Decisions that require Superior approval or inputs
: Strategic initiatives, Resource allocation, Major investments, Organizational changes, external partnerships, and high impact decisions.
Areas of collaboration
: Setting priorities, resource allocation, stakeholder management and decision making
INTERACTIONS
Internal Clients
Leadership team: To understand their strategic priorities and provide data driven insights
Functional heads: To identify data needs, share insights and ensure alignment with business objectives
Data Engineers and Analysts: For data collection, cleaning and preparation
DE/IT Vertical: For ensuring data infrastructure, security and access
Subject Matter Experts: To gain valuable insights and context for data analysis
External Clients
Industry experts: To gain valuable insights and best practices
Data providers: To supplement internal data and broaden the scope of analysis
Academia: To facilitate access to academic research and expertise
Regulatory bodies: To understand industry trends, regulations and compliance requirements
Competitor analysis: To gain insights for competitive intelligence by gathering information on competitor’s strategies, products and market share
DIMENSIONS
Financial Dimensions
Budget: Allocated budget for analytics projects and initiatives
ROI: Return on Investment from data-driven decision projects
Cost Savings: Savings from Process optimization or cost reduction initiatives
Revenue growth: Increase in revenue attributed to data-driven insights and strategies
Profit Margin: Improvement in profit margin due to data-driven decisions
Other Dimensions
Team size and Expertise: Number of team members, skill sets and experience levels
Data Volume: Volume of data processed and analyzed
Data sources: Number and variety of data sources used
Data Quality: Data accuracy, completeness, and consistency metrics
Analytics projects: Number of Analytics projects completed per year
Decision impact: Number of strategic or operational decisions influenced by data-driven insights
Technology adoption: Adoption rate of new analytics tools and techniques
Industry benchmarks: Comparison of performance metrics against industry benchmarks
Educational Qualifications
Essential
: B.Tech / B.E. / BA-Economics, BA-Statistics / MBA/Post Graduation preferably (Statistics / Analytics)
Desirable/ pref
.: Any professional Diploma/ Certification in Statistics / Analytics (CBAP/ CCBA)
Competency Requirement
Behavioral Competency-
Strong Business Acumen, Excellent Communication & Presentation Skills, Stakeholder Management, Team Management (Level 3 Proficiency required in all)
Technical Competency-
Technical knowhow (SQL, Python, and Data visualization tools like Tableau, Power BI etc.) (Level 2 Proficiency required),
Knowledge of Statistical methods for data analysis and modeling
(Level 3 Proficiency required), Familiarity with business intelligence tools for dashboard development and reporting (Level 3 Proficiency required), Understanding of data governance and quality principles (Level 3 Proficiency required)
