Biomedical Scientist jobs in Singapore
Data Scientist (Privacy Engineering & Applied Research)
SECOND TALENT SG PTE. LTD.
Data scientist (Data Practice)
RISKDATA CONSULTING PTE. LTD.
Energy Private Equity Associate: Research, Diligence & Growth
Robert Walters
Data scientist (Data Practice)
REGTECH INSIGHT PTE. LTD.
Discover more opportunities than anywhere else. Find more jobs nowUser Experience Researcher
TUNDRA TECHNICAL SOLUTIONS SINGAPORE PTE. LTD.
Senior AML Data Scientist — Python & Vyntra expert
Unison Consulting Pte Ltd
Connect with headhunters to apply for similar jobsDevelopment Scientist (ROS-I - Robotics AI/Coordination), ARTC
A*STAR RESEARCH ENTITIES
Cell & Gene Therapy Biotechnologist — cGMP, 12-Hour Shifts
Lonza
Manufacturing Biotechnologist - Train and Place Program (Start ASAP)
Prosple
Manufacturing Biotechnologist - Train and Place Program
Roche
Biopharma - Downstream Biotechnologist
PROCESS ENGINEERING SPECIALISTS PTE. LTD.
Biotechnologist (GRIT Programme)
SANOFI MANUFACTURING PTE. LTD.
Senior Solutions Design & Innovation Engineer
SCHENKER SINGAPORE (PTE) LTD
Data Scientist (Privacy Engineering & Applied Research)
Job summary
A technology solutions company in Singapore is seeking a Data Privacy Specialist to enhance national capabilities in privacy technologies. You will lead applied research, collaborate with stakeholders, and design innovative privacy solutions. Candidates should have strong programming skills in Python and a background in data science or machine learning. This role offers an opportunity to work at the intersection of research and practical implementation.
Qualifications
- Minimum 2–3 years of hands-on experience in data science, ML, or applied research.
- Demonstrated experience designing experiments and analyzing model performance.
- Proven ability to interpret and apply academic research to practical solutions.
Responsibilities
- Design and execute experiments to evaluate emerging PETs.
- Collaborate with stakeholders to understand privacy challenges.
- Identify opportunities where PETs can solve cross-agency data challenges.
Skills
Education
Tools
Our Data Privacy Practice advances national capabilities in privacy-enhancing technologies (PETs) and responsible AI adoption. We combine applied research, engineering, and partnership with public-sector organizations to ensure data can be used safely and effectively for public benefit.
The team’s portfolio includes Advanced Anonymisation, Synthetic Data Generation, Homomorphic Encryption, Differential Privacy, Federated Analytics, Federated Learning, and Trusted Execution Environments. We aim to bridge research and implementation, translating complex PETs into practical, deployable solutions.
- Applied Research & DevelopmentDesign and execute experiments to evaluate emerging PETs.
Build proof‑of‑concepts demonstrating real‑world applicability and measurable impact.
Stay current with academic research and translate insights into production‑ready solutions.
Contribute to knowledge‑sharing efforts through technical documentation and publications. - Partnership & ImplementationCollaborate with stakeholders to understand privacy challenges and operational constraints.
Lead pilot programs to validate PET adoption and gather user feedback.
Provide ongoing technical consultation and support through deployment stages. - Solution Design & InnovationIdentify opportunities where PETs can solve cross‑agency data challenges.
Architect scalable, interoperable privacy solutions for large‑scale use.
Define frameworks and best practices for sustainable technology adoption. - Cross‑Functional CollaborationWork with data scientists, engineers, and product managers to align research and implementation.
Ensure solutions comply with evolving data governance and regulatory requirements.
- Bachelor’s degree or higher in Computer Science, Data Science, or related discipline.
- Minimum 2–3 years of hands‑on experience in data science, ML, or applied research.
- Strong programming skills in Python and frameworks such as PyTorch, TensorFlow, or scikit‑learn.
- Demonstrated experience designing experiments and analyzing model performance.
- Proven ability to interpret and apply academic research to practical solutions.
- Experience in PETs (anonymisation, synthetic data generation, differential privacy).
- Knowledge of cloud environments and ML deployment workflows.
- Familiarity with frontend integration (React, Next.js) is a plus.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.