As a Data Scientist, you are a rare blend of analytical rigor and creative problem-solving. You possess deep expertise in predictive modeling, machine learning, and AI agent orchestration, with a strong foundation in SQL, Python, and modern frameworks. You are not only fluent in data and algorithms, but also in business context, translating complex insights into strategic actions that drive measurable outcomes.
The core responsibilities for the job include the following :
Predictive Churn Modeling and Customer Risk Scoring :
- Develop churn risk models using behavioral, transactional, and engagement signals across the customer journey.
- Integrate features from product usage, CRM, support, billing, feedback channels, and many more sources to create a comprehensive view of customer health.
- Build and deploy real-time scoring systems that proactively flag accounts needing attention.
- Design customer segmentation strategies to enable targeted retention campaigns and Support for Retention :
- Leverage language models and agent frameworks to build intelligent assistants that support different teams in the business.
- Deploy AI agents that summarize customer health, provide next-best actions, and auto-generate personalized outreach content.
- Use retrieval and generation techniques to help teams access relevant insights, historical case references, and CS playbooks.
- Experiment with prompt strategies and adaptive workflows to improve the performance of agent-based systems.
Insight Generation and Strategic Enablement :
Conduct deep-dive analyses to understand drivers of retention and long-term customer value.Enable leadership and GTM teams with dashboards, scorecards, and alerts derived from predictive insights.Collaborate on testing strategies that validate the effectiveness of AI-driven touchpoints and communications.Support the development of customer lifecycle metrics that reflect engagement, satisfaction, and risk.Requirements :
8+ years of experience in data science, AI, or ML roles, ideally in SaaS, B2B, or product-led environments.Advanced proficiency in Python and SQL for data processing, analysis, and modeling.Experience developing and deploying models for customer lifecycle events (e. g., churn, engagement, growth).Strong understanding of AI techniques, including both structured ML and LLM applications.Experience with large language models (OpenAI, Claude, open-source), vector databases, and prompt engineering.Familiarity with tools for building and deploying AI agents (e. g., LangChain, semantic search frameworks).Experience integrating insights into operational systems or customer-facing tools.Ability to communicate complex ideas clearly to both technical and non-technical audiences.Experience working with telemetry pipelines, event-based data, or real-time scoring infrastructure.Background in building intelligent assistants, Slack bots, or CRM-integrated AI tools.Understanding of customer health metrics, NPS analysis, or usage clustering.(ref : hirist.tech)