Data Scientist

We're looking for a Data Scientist to own end-to-end ML models and experimentation across the company. This is a high-ownership, execution-heavy role focused on improving conversions, call success rates, and operational efficiency. You'll own problems end-to-end from metrics and experiments to production models and product impact. Responsibilities Owned end-to-end ML systems for an AI-driven outbound call center. Work on problems directly tied to loan sales, collections, and insurance conversions. Drive experiments and product changes with measurable revenue impact. Build user / lead scoring models to decide who to call, when, and how. Improve conversion rates across outbound funnels (dial, connect, conversation, conversion). Work with rich, messy data: call transcripts, recordings, user metadata, and call-level signals. Extract insights from within-call behavior (drop-offs, objections, engagement patterns). Design and run A/B experiments on model + product changes. Improve LLM agent performance (response quality, handling objections, and conversation flow). Identify failure points (bad targeting, poor timing, weak conversations) and solve them using data + ML. Ship models into production and iterate based on real-world performance. Requirements Strong Python + SQL (used daily). Hands-on ML experience (practical projects/internships). Ability to go from raw data to insight model deployment. Comfort working with noisy, real-world datasets (text, behavioral data). Understanding of experimentation / A-B testing. Strong problem-solving + ownership mindset. Tech Stack: Python, ML libraries (sklearn / PyTorch), ClickHouse, LLMs (Claude, internal systems), Experimentation-driven environment. This job was posted by Jay Mangal from Knowl.