Data Scientist

Full Scale

Senior Data Scientist focusing on agentic AI and predictive modeling for a fast-growing Philippine tech company. Working remotely on real-time customer interaction systems in automotive retail.

Posted 7/29/2026contractRemote • 🇵🇭 PhilippinesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying LLM agents, with a strong focus on model lifecycle management, evaluation infrastructure, and practical application of data science in production environments. Proficient in Python, SQL, and AWS ML tooling, with a solid understanding of statistical fundamentals and model impact quantification.

Highest-signal resume keywords

LLM Agent DevelopmentPython ProgrammingSQL ProficiencyAWS ML ToolingModel Lifecycle Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceModel DeploymentFeature EngineeringStatistical FundamentalsPredictive ModelingRAG SystemsTool Use and Function CallingAdversarial TestingRegression GatesConfidence Thresholds

Soft Skills

CommunicationProblem-Solving

Tools & Technologies

BedrockSageMakerLambdaOpenSearch ServerlessPgvectorLakehouseData Warehouse

Industry Keywords

Dealership SystemsCustomer Identity ResolutionService HistoryOEM DocumentationInventory Pricing

Tech Stack

Tools & technologies

AWSPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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