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Data Scientist - Job Opportunity at ACTXA PTE. LTD.

Singapore, Singapore
Full-time
Mid-level
Posted: May 24, 2025
On-site
SGD 5,000 - 6,500 per month

Benefits

Comprehensive employee wellness program designed to support holistic health and work-life integration, demonstrating the company's commitment to preventive healthcare principles in their own workplace culture

Key Responsibilities

Lead end-to-end data collection, processing and preprocessing initiatives to transform raw digital health data into high-quality analytical assets that drive strategic healthcare insights and population health improvements
Architect and implement advanced statistical techniques, AI and machine learning algorithms to analyze complex healthcare datasets, directly contributing to breakthrough preventive health interventions that reduce healthcare costs and improve patient outcomes
Drive feature engineering excellence and develop sophisticated machine learning models that accurately predict health risks and outcomes, enabling proactive healthcare delivery and chronic disease prevention at scale
Champion productization efforts by deploying predictive models into scalable, production-ready solutions while implementing automated data pipelines that enhance operational efficiency and accelerate time-to-market for healthcare innovations
Establish comprehensive documentation frameworks and deliver executive-level reports that translate complex data science findings into actionable business intelligence for strategic decision-making across clinical and commercial stakeholders
Foster cross-functional collaboration with clinicians, data engineers, and product managers to align data science initiatives with business objectives while effectively communicating complex technical insights to diverse stakeholder groups
Spearhead continuous innovation initiatives by staying at the forefront of data science, machine learning, and healthcare technology advancements to maintain competitive advantage in the preventive healthcare market

Requirements

Education

Bachelor or Master degree in Data Science, Biostatistic, Mathematics, Biomedical Engineering, Electrical & Electronics Engineering, Computer Science or related discipline

Experience

3+ years of experience in data science, healthcare analytics, or preventive medicine

Required Skills

Proficiency in programming languages: Python, R, SQL Experience with machine learning frameworks: TensorFlow, PyTorch, Scikit-learn Good understanding of biostatistics, epidemiology, and research design Familiarity with cloud-based platforms: AWS & Ali Cloud Experience with wearable device data, signal and noise processing Keen interest in Chronic disease prevention, Population health management and Digital health interventions Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment Natural tenacity, resilience, and determination
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Sauge AI Market Intelligence

Industry Trends

The digital health market is experiencing unprecedented growth with global healthcare analytics market projected to reach $84.2 billion by 2027, driven by increasing adoption of AI/ML in preventive medicine and population health management. Singapore's position as a leading healthcare innovation hub in Asia-Pacific creates significant opportunities for data scientists specializing in health technology applications. Wearable device integration and IoT health monitoring solutions are becoming mainstream, with chronic disease prevention shifting from reactive to predictive models. The COVID-19 pandemic accelerated digital health adoption by 5-7 years, creating sustained demand for professionals who can translate health data into actionable preventive interventions. Regulatory frameworks around health data analytics are evolving rapidly, with increased emphasis on evidence-based digital therapeutics and AI-driven clinical decision support systems. Companies focusing on preventive healthcare are attracting significant venture capital investment, positioning data scientists as critical strategic assets in this transformation.

Salary Evaluation

The offered salary range of SGD 5,000-6,500 per month (approximately USD 45,000-58,500 annually) appears below market rate for mid-level data scientists in Singapore's competitive market. Typical compensation for similar roles ranges SGD 7,000-10,000 monthly, suggesting potential for negotiation or additional equity compensation given the company's growth stage and deep tech focus.

Role Significance

Typical data science teams in similar-sized deep tech healthcare companies range from 3-8 professionals, with this role likely working closely with 2-3 data engineers, 1-2 clinical researchers, and cross-functional product teams. The collaborative nature suggests a flat organizational structure with direct access to senior leadership and clinical stakeholders.
This mid-level position carries substantial strategic impact, functioning as both individual contributor and technical leader in shaping the company's AI-driven healthcare solutions. The role involves direct influence on product development and clinical outcomes, with responsibilities spanning from research to production deployment, indicating significant autonomy and decision-making authority.

Key Projects

Development of predictive models for diabetes and hypertension risk assessment using multi-modal health data from wearable devices, clinical records, and lifestyle factors Implementation of real-time health monitoring systems that provide personalized preventive care recommendations based on continuous physiological data streams Creation of population health analytics platforms that identify at-risk cohorts and enable targeted interventions for chronic disease prevention at scale

Success Factors

Deep technical expertise in healthcare-specific machine learning applications combined with strong understanding of clinical workflows and regulatory requirements for medical device software. Success requires ability to bridge the gap between complex algorithms and practical clinical implementation while maintaining rigorous validation standards. Exceptional communication skills to translate technical findings into actionable insights for clinical teams, regulatory bodies, and business stakeholders. The ability to present complex statistical models and their clinical implications to diverse audiences is crucial for driving adoption and securing regulatory approval. Proven ability to work with noisy, incomplete healthcare data while maintaining high standards for model validation and clinical safety. Experience with real-world evidence generation and clinical study design principles becomes essential for developing commercially viable healthcare AI solutions. Strong project management capabilities to coordinate multi-disciplinary teams and manage complex product development timelines in a regulated environment. Success requires balancing innovation speed with clinical rigor and regulatory compliance requirements.

Market Demand

High demand with specialized premium - Healthcare data scientists with AI/ML expertise command significant market value due to the intersection of technical complexity and domain expertise required. The specific focus on preventive medicine and chronic disease management represents a high-growth niche with limited qualified professionals.

Important Skills

Critical Skills

Python and R programming proficiency represents foundational capability for healthcare data analysis, with Python particularly essential for deep learning frameworks and production deployment. Healthcare data science requires handling complex, multi-dimensional datasets where programming efficiency directly impacts research outcomes and product development timelines. Machine learning frameworks expertise (TensorFlow, PyTorch, Scikit-learn) enables development of sophisticated predictive models for health risk assessment and intervention optimization. Healthcare applications demand robust model validation and interpretability, making framework proficiency crucial for regulatory compliance and clinical adoption. Biostatistics and epidemiology knowledge provides essential foundation for understanding health data patterns, clinical study design, and evidence generation requirements. This domain expertise distinguishes healthcare data scientists from general practitioners and ensures clinical relevance of analytical approaches. Cloud platform experience (AWS, Ali Cloud) enables scalable deployment of health analytics solutions while maintaining data security and regulatory compliance. Healthcare data processing requires robust infrastructure for handling sensitive patient information and supporting real-time clinical decision support systems.

Beneficial Skills

Signal processing and wearable device data analysis skills become increasingly valuable as continuous health monitoring expands. Expertise in filtering noise from physiological signals and extracting meaningful biomarkers positions professionals for advanced roles in digital biomarker development and regulatory science. Clinical workflow understanding and healthcare domain knowledge accelerate professional impact by enabling more effective collaboration with clinical teams and faster identification of practical implementation challenges. This knowledge bridges technical capabilities with real-world healthcare delivery requirements. Regulatory science familiarity and experience with medical device development processes provide competitive advantage in healthcare AI roles. Understanding FDA approval pathways, clinical evidence requirements, and quality management systems enables more effective product development and faster market entry.

Unique Aspects

Direct impact on population health outcomes through development of AI-driven preventive healthcare solutions that address major chronic diseases affecting millions globally, providing meaningful purpose-driven work environment
Opportunity to work with cutting-edge wearable device data and multi-modal health analytics, positioning professionals at the forefront of digital health innovation and personalized medicine development
Access to diverse international research collaborations and clinical partnerships, enabling exposure to global healthcare challenges and solutions while building valuable professional networks in the healthcare AI ecosystem
Experience with end-to-end product development from research to clinical validation to commercial deployment, providing comprehensive understanding of healthcare technology development lifecycle and regulatory processes

Career Growth

Career progression to senior individual contributor roles typically occurs within 2-3 years given the specialized healthcare domain expertise. Management track positions generally require 3-5 years of healthcare data science experience, while executive roles typically require 7-10 years with demonstrated success in product development and regulatory approval processes.

Potential Next Roles

Senior Data Scientist or Principal Data Scientist roles focusing on strategic AI/ML initiatives and technical leadership within healthcare organizations Clinical Data Science Manager positions overseeing teams developing evidence-based digital health solutions and managing regulatory submission processes Healthcare AI Product Manager roles bridging technical development with commercial strategy and clinical implementation Chief Data Officer or Head of AI positions in healthcare technology companies, particularly those focusing on preventive medicine and population health

Company Overview

ACTXA PTE. LTD.

ACTXA operates as a deep technology company established in 2015, positioning itself at the intersection of artificial intelligence and preventive healthcare innovation. The company's focus on evidence-based, personalized health insights across multiple health domains indicates a comprehensive platform approach rather than point solution development, suggesting significant technological sophistication and clinical research capabilities.

As a Singapore-headquartered company with international partnership focus, ACTXA appears positioned as a regional leader in AI-driven preventive healthcare solutions. The company's emphasis on research collaborations and evidence-based approaches suggests strong academic and clinical partnerships, while their focus on population health indicates scalable business model potential.
Singapore's strategic position as Asia-Pacific's healthcare innovation hub provides ACTXA with access to diverse patient populations, advanced healthcare infrastructure, and supportive regulatory environment for health technology development. The location offers significant advantages for expansion into regional markets while maintaining proximity to leading medical institutions and research facilities.
The company's commitment to employee wellness programs and environmental impact suggests a values-driven culture aligned with their healthcare mission. The emphasis on continuous innovation and employee potential realization indicates a learning-oriented environment with significant professional development opportunities, typical of deep tech companies prioritizing talent retention and technical excellence.
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