4400294-Manager

Description
Job Purpose This role is placed under the 'Data Insights' team of EXL's Emerging Business Unit (EBU) which is an amalgamation of Utilities, Travel, Transportation, Banking and F&A businesses. The 'Data Insights' team will play a critical role in identifying key data sources, creating strong data ingestion and management practices, generating data-driven insights and presentations. This is with the objective of optimizing operations, improving end-customer experience, generating client business insights, support continuous improvement, drive transformation leading to eventual growth of the business.  We are seeking a skilled Data Analyst who has a strong foundation in data analytics, experience working with large datasets, and expertise in analysing operational performance and business metrics. A good understanding of analytics, insight and visualization tools and techniques will be preferred to generate key data storytelling and communication to stakeholders. Skills & Experience Essential skills and experience - minimum criteria  Education: Bachelor's degree (any)  Experience: 3+ years of experience in data analysis, preferably within a BPM, or voice process environment. Proven experience in analyzing operational metrics Technical Skills: Proficiency in data analytics tools and platforms such as SQL , Excel . Experience with scientific analytics tools like Python , R , or SAS for statistical analysis and model development. Competent with data visualization tools such as Tableau or Power BI . Knowledge of statistical techniques, including regression analysis, hypothesis testing, clustering, and machine learning. Familiarity with natural language processing (NLP) techniques and tools for analyzing voice data. Soft Skills: Strong analytical thinking and problem-solving skills. Ability to communicate complex data insights clearly and effectively to non-technical stakeholders both verbally and through presentations. Excellent organizational skills, attention to detail, and ability to manage multiple tasks simultaneously. Desirable skills and experience  Education in Statistics, Computer Science, Operations Research, or a related field. Advanced degree or certification in Data Analytics, Business Intelligence, or related fields is a plus . Experience and knowhow in the transportation industry and/ or a voice environment is a plus . Responsibilities  Data Collection and Analysis: Gather, clean, and analyze data from various sources, including performance metrics, customer satisfaction (CSAT) scores, and operational KPIs. Develop efficient ways of managing and storing large data sets Work with different stakeholders to ensure the data is accurate, accessible, and aligned with business goals. Conduct exploratory data analysis (EDA) to uncover trends, patterns, and insights in voice process operations. Evaluate call volumes, average handling times (AHT), first call resolution (FCR), and other non-voice metrics to identify operational gaps. Familiarity with natural language processing (NLP) techniques and tools for analyzing voice data. Reporting and Dashboards: Develop and maintain regular and ad-hoc reports and dashboards to monitor performance, agent productivity, and customer experience.  Provide actionable insights through clear visualizations and presentations to clients, management and cross-functional teams using tools such as Tableau and/or Power BI. Root Cause Analysis: Perform in-depth analysis to identify the root causes of process inefficiencies, customer dissatisfaction, and operational bottlenecks. Use data to identify areas for improvement, such as call routing, agent performance, and resolution times. Scientific Analytics and Statistical Techniques: Apply scientific analytics tools and techniques, such as regression analysis, hypothesis testing, and A/B testing. Work with predictive models to forecast call volumes, staffing needs, AHTs and potential customer outcomes based on historical data. Optimization: Collaborate with operations managers, SMEs, quality teams, WFM and other stakeholders to implement data-driven strategies for improving processes and enhancing customer service. Use data to drive decisions related to process automation, resource allocation, and quality improvements. Stakeholder Collaboration: Present and communicate complex data findings in a clear, concise and actionable manner to both technical and non-technical stakeholders. Craft presentations with clear and powerful messages and cohesive story telling Continuous Improvement: Monitor and track the relevance, impact and feedback of previous insights and continuously refine data models, metrics, and reporting strategies to support ongoing process optimization. Stay updated on the latest trends in relevant industries, and scientific analytics techniques.

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