2026-7839 Data Scientist-Mid (Midshift) at Arch Global Services (Philippines) Inc.

Taguig, Metro Manila, Philippines -

Full Time

Start Date

Immediate

Expiry Date

02 Jun, 26

Salary

0.0

Posted On

04 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Foundation Models, Azure, Databricks, Python, Git, Prompt Engineering, Data Extraction, Classification, Routing, Search, Decision-Making, Statistical Analysis, Evaluation Frameworks, Communication, Systems Thinking

Industry

Outsourcing and Offshoring Consulting

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 Mid-Level Data Scientist delivers solutions for insurance business problems using foundation models and data science fundamentals. They take initiative, own their work with periodic guidance, and proactively communicate progress, blockers, and decisions with team and leadership. They demonstrate growing systems thinking skills-understanding component interactions, measuring system performance, and identifying improvement opportunities. They work in Azure/Databricks using Python with git version control and produce maintainable code with clear documentation. Responsibilities: Collaborate with scientists, engineers, product owners, and business customers to translate business problems into technical solutions Deliver solutions for data extraction, classification, routing, search, and decision-making using foundation models with guidance on approach 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 Implement evaluation frameworks using precision, recall, F-1 scores, accuracy, and operational metrics with guidance Contribute to documentation and support junior team members as peer collaborator Break down well-scoped problems into measurable components and identify trade-offs between approaches Qualifications Strong communication skills that convey statistical and business impact, proactively surface issues, and keep collaborators and leaders informed Proficiency with Python from a functional programming paradigm, including dependency management, virtual environments, and git version control Experience or strong familiarity with cloud platforms like Azure and Databricks using foundation model APIs (OpenAI, Anthropic, Google, etc.) Working familiarity with ML fundamentals (supervised/unsupervised learning, evaluation metrics, model validation) and statistical methods Experience implementing solutions with foundation models, including prompt engineering and output validation Demonstrated capability to execute well-scoped projects with periodic guidance, iteratively refining through diagnosis and hypothesis testing 2-5 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 Bachelor's or graduate degree in quantitative field with systems thinking exposure (Computer Science, Statistics, Economics, Physics, Mathematics, Operations Research, Computational Linguistics) Insurance industry exposure Experience with agent frameworks (LangChain, LlamaIndex), RAG systems, or vector databases Experience with evaluation frameworks, experimental design, or production ML monitoring

Responsibilities

The Mid-Level Data Scientist will deliver solutions for insurance business problems utilizing foundation models and data science fundamentals, taking initiative and communicating progress proactively. Responsibilities include collaborating on technical solutions, delivering models for data extraction, classification, routing, search, and decision-making, and engineering/evaluating foundation model prompts.