Posted at: 29 January

2026-7843 Data Scientist-Senior

Company

CompanyArch Global Services (Philippines) Inc.

Arch Global Services (Philippines) Inc. is a Taguig City-based B2B provider of operational support services, specializing in IT and business process outsourcing for the insurance and reinsurance industries, serving global markets including the US and Europe.

Remote Hiring Policy:

Arch Global Services (Philippines) Inc. hires exclusively within the Philippines, with no current support for remote work options. All roles are based in physical office locations in Taguig City, Quezon City, and Cebu City.

Job Type

Full-time

Allowed Applicant Locations

Philippines

Salary

$100,000 to $150,000 per year

Job Description

Company Description

AGSI was incorporated in April 2016. We are committed to supporting the goals of Arch divisions through exceptional service delivery. We pride ourselves on maintaining flexibility and responsiveness to adapt to business unit and industry demands while focusing on sound project management. We are dedicated to growing and developing our employees as we build strong teams with strategic leadership.

Job Description

The ideal Senior Data Scientist delivers production-quality solutions for insurance business problems using foundation models and data science fundamentals. They take initiative, own their work from problem definition through deployment, and proactively communicate progress, blockers, and considerations with the team and leadership. They excel at systems thinking-decomposing complex problems, designing measurement frameworks, and optimizing across multiple dimensions. They work in Azure/Databricks using Python with git version control and produce maintainable code with strong documentation.

Responsibilities:
 

  • Collaborate with scientists, engineers, and technical product owners to create technical solutions · Deliver production solutions for data extraction, classification, routing, search, and decision-making using foundation models
  • Manipulate and analyze data programmatically, derive statistically sound insights, and communicate findings that address technical and business considerations
  • Engineer and evaluate foundation model prompts systematically across domain datasets
  • Design comprehensive evaluation frameworks using precision, recall, F-1 scores, accuracy, and operational metrics
  • Own documentation and mentor junior team members through systematic problem-solving approaches
  • Decompose complex problems into measurable components, optimize across multiple dimensions, and articulate trade-offs

Qualifications

  • Strong collaboration skills, capable of conveying statistical performance and business considerations and proactively surface issues to keep technical personas informed
  • Strong Python proficiency from a functional programming paradigm, including dependency management, virtual environments, and git version control following git flow practices
  • Proven experience with cloud platforms like Azure and Databricks using foundation model APIs (OpenAI, Anthropic, Google, etc.)
  • Deep familiarity with ML fundamentals (supervised/unsupervised learning, evaluation metrics, model validation) and statistical methods
  • Experience designing and implementing solutions with foundation models, including prompt engineering and output validation
  • Proven capability to deliver solutions from inception through production with variable autonomy, iteratively refining through diagnosis, hypothesis testing, and systematic improvement
  • 5-15 years of relevant professional experience in data science, machine learning, or related fields; advanced graduate research or academic work may substitute for professional experience

Additional Information

  • Graduate degree in quantitative field with strong systems thinking emphasis (Computer Science, Statistics, Economics, Physics, Mathematics, Operations Research, Computational Linguistics) · Insurance industry experience with document processing
  • Experience with agent frameworks (LangChain, LlamaIndex), multi-agent orchestration, RAG systems, or vector databases
  • Experience with production ML monitoring, experimental design, self-healing systems, or automated optimization