Data Scientist

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Full Scale

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

💼 Consulting

☁️ SaaS

🏢 Enterprise

Consulting • SaaS • Enterprise

Full Scale is a staff augmentation company that focuses on providing exceptional tech talent to businesses across various industries. Founded by Matt Watson, a seasoned tech entrepreneur, Full Scale has partnered with over 200 tech companies since its inception, offering top-notch services while fostering a positive work environment for its employees. The company's commitment to corporate social responsibility is evident in its engagement in community outreach, environmental sustainability, and educational initiatives.

📋 Description

• Design, build, and evaluate LLM agents that operate against real dealership systems — booking service appointments, answering vehicle availability questions, resolving customer identity, and escalating to humans with full context. • Own the tool-use layer: define the tools and function schemas agents call, the guardrails around each, and how the system fails when a downstream service is slow or unavailable. • Build agent evaluation infrastructure — offline eval sets, adversarial and edge-case suites, live A/B testing, and regression gates that block deployment on quality drops. • Design and implement escalation logic and confidence thresholds: where the agent acts, where it confirms, and where it hands off to a person. • Develop and maintain RAG systems over dealership content (service history, OEM documentation, policy, inventory) using Bedrock embeddings, pgvector on Aurora, and OpenSearch Serverless. • Own prompt architecture, versioning, and change control as a first-class engineering artifact under source control. • Build, validate, and deploy predictive models on lakehouse data — gross profit forecasting, customer lifetime value, defection risk, next-service prediction, identity resolution, and inventory pricing signals. • Own the full model lifecycle: feature engineering, training, validation, deployment, monitoring, and retraining. • Ship models with drift and degradation monitoring from day one. • Convert business questions from operations and ownership into well-posed modeling problems and push back when a question is better answered with a query than a model. • Quantify and communicate model impact in dealership terms: gross, units, retention, CSI, labor hours saved.

🎯 Requirements

• 4+ years applying data science in production, with models that made real decisions and had real consequences • Strong Python and SQL. You write code others can run and maintain. • Hands-on experience building LLM agents with tool use and function calling — not just prompt engineering. • Be ready to walk through a system you built and how you evaluated it. • Practical RAG experience: embeddings, vector search, chunking, retrieval evaluation, and re-ranking. • Sound statistical fundamentals and honest handling of uncertainty. We prefer a well-calibrated interval over a confident point estimate. • Experience deploying models to production — not handing notebooks off to an engineering team. • Working comfort with AWS ML tooling (Bedrock, SageMaker, Lambda) and a lakehouse or data warehouse environment.

🏖️ Benefits

• Fully remote – work from anywhere in the Philippines • Work on live agentic AI systems — not POCs, not slideware, not handoff-to-engineering • Data layer already in place (AWS lakehouse, Databricks) so you can focus on modeling and agents, not plumbing • Small, senior, high-autonomy team with documentation-first culture • Opportunity to define the evaluation and deployment standards every new model and agent will follow • A team environment that values intellectual honesty, technical depth, and follow-through