Senior Data Scientist

Summary

We are looking for a Data Scientist with a strong focus on Data Modeling to join our Customer Experience (CX) BI and Analytics team, that supports Customer Success, Professional Services, Customer Support, and Renewals. Data Modeling and Analytics is the core responsibility of this role. You will design, build, and continuously improve data models including time series forecasting, regression models, capacity planning, and quota/target forecasting that enable proactive decision-making across CX functions. This role is ideal for early-career professionals aspiring to build deep expertise in applied analytics while solving real business problems, working on operational data.

Key Responsibilities

Data Modeling and Forecasting 

• Develop, maintain, and enhance data models for CX use cases
• Support ticket inflow and backlog forecasting
• Capacity and staffing planning for Support and Professional Services
• Renewal, churn, and retention forecasting
• Utilization and delivery forecasting for Professional Services
• Apply time series techniques, regression analysis, and statistical modeling.
• Perform model validation, back-testing, and error analysis; continuously refine models based on business feedback.

Analytical Data Preparation

• Use SQL and Python to extract, clean, and transform data required for data models.
• Perform feature engineering and exploratory data analysis to identify drivers of CX outcomes.
• Ensure datasets used for modeling are accurate, consistent, and well-documented.

Business Insight Communication

• Use Excel/Google Sheets for model validation, scenario analysis, and what-if simulations.
• Build Tableau dashboards to visualize forecasts, trends, and model outputs.
• Translate data insights into clear, actionable recommendations for the business.

Stakeholder Collaboration

• Partner closely with teams across CS, PS, GS, and Renewals to understand planning/forecasting needs.
• Explain modeling assumptions, limitations, and insights in business-friendly language.
• Support CX planning cycles (capacity planning, renewal forecasting, operational reviews).
• ML Ops:
• Build and maintain automated CI/CD pipelines for machine learning
• Implement systems to track model drift and data quality in production
• Optimize model latency and throughput for high-scale applications
• AI Implementation:
• Build and maintain AI based Predictive solutions
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