Senior data analyst

4 weeks ago

Bangalore, India Responsive Full time

About Responsive Responsive (formerly RFPIO) is the global leader in strategic response management software, transforming how organizations share and exchange critical information. The AI-powered Responsive Platform is purpose-built to manage responses at scale, empowering companies across the world to accelerate growth, mitigate risk and improve employee experiences. Nearly 2,000 customers have standardized on Responsive to respond to RFPs, RFIs, DDQs, ESGs, security questionnaires, ad hoc information requests and more. Responsive is headquartered in Portland, OR, with additional offices in Kansas City, MO and Coimbatore, India. Learn more at responsive.io. About the Role The Senior Data Analyst - Operations for a Saa S company plays a pivotal role in leveraging data to optimize operational processes, drive efficiency, and improve overall business performance. This role involves gathering, analyzing, and interpreting operational data to identify trends, reduce inefficiencies, and support strategic decision-making. The Senior Data Analyst will work closely with cross-functional teams such as product, marketing, sales, customer success, finance, and engineering to ensure the company's operational goals are data-driven and aligned with business objectives. The ideal candidate will have experience in Saa S operations and a strong background in data analysis, using advanced analytics tools to deliver actionable insights. Essential Responsibilities Operational Data Collection & Management: Gather, clean, and organize data from various sources including CRM systems, customer usage data, billing platforms, and internal operational tools. Ensure the integrity, accuracy, and consistency of operational data across departments. Collaborate with IT and data engineering teams to optimize data pipelines, ETL processes, and data storage systems. Data Analysis & Insights: Analyze operational data to identify inefficiencies, trends, and opportunities for improvement, focusing on areas like customer onboarding, product usage, renewals, and churn. Provide data-driven recommendations to streamline Saa S operations, reduce costs, and improve product delivery efficiency. Conduct root cause analysis to understand operational bottlenecks and provide solutions to improve performance. Performance Metrics & KPI Tracking: Define and track key performance indicators (KPIs) related to Saa S operations, including customer satisfaction (NPS), support resolution times, system uptime, and subscription renewal rates. Regularly report on operational KPIs to senior leadership, translating data into actionable insights. Develop and maintain dashboards and visualizations using tools such as Tableau, Power BI, or Looker to give teams real-time visibility into operational performance. Process Improvement & Operational Efficiency: Collaborate with the operations, finance, and product teams to identify process inefficiencies and recommend solutions for process improvements. Use data to support initiatives aimed at improving the Saa S customer lifecycle, from acquisition through retention and churn reduction. Analyze customer behavior patterns to optimize customer success efforts and improve engagement with the platform. Forecasting & Predictive Analytics: Use statistical models and machine learning techniques to forecast operational trends, such as customer churn, revenue growth, and support demand. Provide forward-looking insights to support resource planning, including customer support staffing, infrastructure scaling, and demand forecasting. Develop and improve predictive models to optimize decision-making in operations. Automation & Technology Integration: Identify and implement automation opportunities in operational workflows to reduce manual effort and increase scalability. Work with engineering and IT teams to integrate operational data from various Saa S tools (e.g., Salesforce, Gainsight, Zendesk ) into centralized analytics platforms. Stay updated on new technologies and tools that can enhance operational efficiency and data analysis capabilities. Collaboration & Stakeholder Engagement: Partner with teams across the organization, including customer success, product management, finance, and engineering, to align data initiatives with business objectives. Present findings, reports, and recommendations to senior leadership in a clear and concise manner. Support leadership in data-driven decision-making and strategic planning for operational improvements. Education Bachelor’s degree in Data Science, Statistics, Computer Science, Operations Research, or a related field; Master’s degree preferred. Experience 10+ years of experience in data analysis, with at least 4+ years in a Saa S or technology environment. Proficiency in SQL for querying databases and working with large datasets. Experience with data analysis tools such as Python, R, or Excel. Expertise in data visualization platforms such as Tableau, Looker, or Power BI. Familiarity with Saa S business models and metrics (MRR, ARR, CAC, LTV, churn). Experience working with Saa S tools such as Salesforce, Hub Spot, Zendesk, or similar. Knowledge of statistical analysis and predictive modeling techniques. Experience working in Saa S or technology-focused companies is highly desirable. Certifications in data analysis, such as Google Data Analytics Professional, or experience with Lean/Six Sigma methodologies is a plus. Knowledge, Ability & Skills Strong analytical and problem-solving skills with the ability to work with complex datasets. Excellent communication skills, with the ability to translate data insights into business recommendations. Detail-oriented with strong organizational skills. Ability to collaborate effectively across departments and work in a fast-paced environment. Self-motivated and proactive in driving data-driven improvements.

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