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)