Data Analyst

Advanced MedTech; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

Advanced MedTech Holdings (AMTH) is seeking a Data Analyst to turn operational data into actionable intelligence for AI products. You will own data quality, standardize classifications, and ensure reliable analytics across regional sources to drive adoption and performance.

Reporting to the Senior Manager, Enterprise Digital Products, you will define data standards, track usage metrics, and collaborate with cross-functional teams to deliver trusted data for decision-making and future predictive

Qualifications

• Bachelor's degree in data science, statistics, computer science, engineering, or a related field.
• Minimum 3 years' experience in data or product analytics, or a similar role.

Responsibilities

• Own the quality and usability of operational data feeding AI products.
• Define classification schema and data standards with product leads.
• Proactively identify and resolve data inconsistencies across sources.
• Own product analytics: track usage, adoption and engagement.
• Translate usage insights into clear recommendations to improve adoption and performance.
• Build and maintain dashboards and reporting for the team and stakeholders.
• Work in an agile, digitally focused project team with global impact.

Skills

SQL

Python

BI / dashboards

Data quality

AI / LLM data

Product analytics

Data visualization

Cross-functional collaboration

Education

Bachelor's degree in data science / statistics / CS / engineering

3+ years in data or product analytics

Tools

BI tools

Data pipelines

Job description

Advanced MedTech Holdings (AMTH) is one of the world’s first fully integrated urology company, driving innovation in urological care through minimally invasive solutions and global reach. Headquartered in Singapore and operating in over 100 countries, AMTH is a leading market leader in stone lithotripsy and manages the world’s largest online urology community.

The Group combines German engineering excellence with advanced digital and clinical capabilities aiming to improve patient outcomes and support healthcare professionals across the continuum of care. Its portfolio includes renowned brands such as Dornier MedTech, WIKKON, Northern Litho, NextMed, and AMT Manufacturing.

With over 1,000 employees worldwide, AMTH aims to continues to expand its impact through strategic investments in R&D, manufacturing, and partnerships— contributing to the evolving the standard of care in endourology and shaping the future of medical technology.

AMTH is a wholly owned subsidiary of Temasek. For media inquiries or more information, visit www.advanced-medtech.com. AMTH complies with applicable data privacy laws; to learn more, please refer to our Privacy Policy.

Position Summary:

The Data Analyst turns operational data into the intelligence that makes our AI products better. You will own the quality and usability of the data feeding our products and turn product-usage data into the analytics that grow adoption and sharpen performance. By ensuring trusted data and actionable analytics, you will help drive the effectiveness of internal AI products that support business decision-making, adoption, and future predictive capabilities.

Reporting to the Senior Manager, Enterprise Digital Products, you will define the data standards and classification schema with the product lead and cross functional teams to ensure data quality across multiple regional sources, and track the usage and engagement metrics that show the team where to grow adoption and where to optimise.

It is a role for someone rigorous and curious, who cares as much about clean, reliable data as about the product decisions that data can drive.

Job Responsibilities:

• Own the quality and usability of the operational data feeding our AI products, ensuring they are clean, standardized, and analysis-ready.
• Define the classification schema and data standards with the Senior Manager, Enterprise Digital Products, and validate data against them throughout the pipeline.
• Safeguard data quality by proactively identifying, investigating and resolving inconsistencies, anomalies and data integrity issues across multiple sources, ensuring accuracy, completeness and reliability of data used by our AI products.
• Own product analytics: track usage, adoption and engagement; surface where and why users engage or drop off.
• Translate usage insight into clear recommendations that improve adoption and product performance.
• Build and maintain dashboards and reporting for the team and stakeholders.
• Work in an agile, digitally focused project team with global impact.

Job Requirements:

1.Education / Qualification

• Bachelor’s degree in data science, statistics, computer science, engineering, or a related field.
• Minimum 3 years' experience in data or product analytics, or a similar role.

2.Experience

• Proficiency in SQL, Python (or equivalent), and BI/dashboard tools, with the ability to extract, transform, analyze, and visualize data to generate actionable insights.
• Experience with data quality, classification and large, multi-source datasets.
• Familiarity with AI/LLM products and the data that powers them, such as usage, evaluation, or classification data.
• Experience defining, tracking and interpreting product usage, adoption and engagement metrics.
• Strong analytical and problem-solving instincts, with the ability to turn messy, multi-source data into clear and actionable insights.
• Works with a high degree of accuracy and discipline, maintaining data quality and consistency across datasets.
• Curious about how data influences AI product performance, user adoption and business outcomes.
• Collaborative and team-oriented, with the confidence to challenge constructively and the openness to learn from others.
• Takes ownership, shows initiative, and is committed to building deep expertise while growing with the team.

4. Operational Skills

• Strong command of written English, with the ability to communicate insights clearly and concisely.
• Able to present data findings and recommendations effectively to technical and non-technical audiences.
• Comfortable working with large, complex, and multi-source datasets.
• Effective collaborator who can work across product, technical, and business teams.