Data Scientist

• Lead end to end development of predictive models, personalization engines, risk scoring, forecasting, and optimization frameworks.
• Oversee ML architecture, feature stores, pipelines, model retraining, validation, monitoring, and governance.
• Implement production grade ML Ops aligned with real time decisioning layers (Azure, Databricks, event-driven triggers, IOE).
• Build a roadmap for intelligent automation and generative AI powered capabilities.
• Own the reporting and analytics stack across the company (Power BI, Snowflake, Databricks, APIs, data ingestion).
• Deliver executive dashboards, operational reporting, forecasting tools, and KPI measurement frameworks.
• Partner with Data Engineering to define data models, semantic layers, governance, quality checks, and observability.
• Lead design and deployment of automation initiatives using n8n, Make.com, Azure Logic Apps, Zapier, and custom Python workflows.
• Automate high value internal workflows across Operations, Care, Sales, Finance, and Engineering.
• Implement "vibe coding" automations, lightweight, rapid build, human in the loop AI automations that accelerate internal productivity.
• Drive adoption of event driven and AI-assisted process automation frameworks.
• Build and mentor a high-performing team of data scientists, ML engineers, BI analysts, and automation developers.
• Establish standards for experimentation, documentation, reproducibility, and operational excellence.
• Own the cross-company AI/automation roadmap, prioritization, resource planning, and execution.
• An advisor to executives on data strategy, AI capabilities, automation opportunities, and business impact.

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