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.