Enable job alerts via email!
Generate a tailored resume in minutes
Land an interview and earn more. Learn more
A progressive technology company is seeking a Data Scientist with Data Engineering skills to lead data-driven analytics solutions. This role involves developing machine learning models, optimizing data pipelines, and collaborating with cross-functional teams to enhance business intelligence. Ideal candidates will have a strong educational background and practical experience in both data science and engineering, particularly in healthcare and technology environments.
As a Mid to Senior Data Scientist with Data Engineering Skills, you will lead the design and development of data-driven solutions by leveraging your expertise in machine learning, statistical analysis, and scalable data infrastructure. You’ll be expected to work independently and collaboratively with cross-functional teams, influence data strategy, and contribute to shaping analytics architecture. This role demands a strong blend of analytical mindset and engineering rigor to drive real-world impact from data.
As a Mid to Senior Data Scientist with Data Engineering Skills, you will lead the design and development of data-driven solutions by leveraging your expertise in machine learning, statistical analysis, and scalable data infrastructure. You’ll be expected to work independently and collaboratively with cross-functional teams, influence data strategy, and contribute to shaping analytics architecture. This role demands a strong blend of analytical mindset and engineering rigor to drive real-world impact from data.
website link: https://svrtech.com.my/
Develop and deploy machine learning models to support diagnostic insights, predictive patient outcomes, predictive equipment failures, operational forecasting, and etc.
Conduct in-depth data analysis to identify trends and generate actionable business insights, especially from medical and laboratory data, and data from other industries.
Collaborate with medical experts, product managers, and business analysts to define use cases and success metrics.
Communicate complex data concepts clearly to both technical teams and non-technical stakeholders in healthcare and other industries.
Design, build, and optimize scalable data pipelines and architectures to support data science workflows and real-time analytics.
Ingest and process large volumes of structured and unstructured data from diverse sources such as diagnostic devices, EMRs, and IoT systems.
Ensure high standards of data quality, security, and compliance—especially within regulated healthcare environments.
Work with DevOps and software engineers to operationalize models and data products into production systems.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related discipline.
3 to 5+ years of experience in data science, with at least 2–3 years of hands-on experience in data engineering or backend data workflows.
Proficient in Python (Pandas, NumPy, scikit-learn) and SQL.
Experience using Tableau or other programmers that are similar in nature.
Strong grasp of machine learning, deep learning, and statistical modeling.
Experience with data pipeline tools and frameworks: Apache Airflow, Spark, Kafka, or similar.
Deep understanding of cloud platforms (AWS/GCP/Azure), especially cloud-based data services.
Familiarity with CI/CD pipelines, Docker, and container orchestration (e.g., Kubernetes).
Exposure to modern data platforms: Snowflake, Databricks, BigQuery, etc.
Ability to work independently and take ownership of end-to-end solutions.
Strong communication skills, especially in translating complex data concepts into business language.
Mentoring experience or leadership in guiding junior data scientists or engineers is a plus.
Comfortable operating in fast-paced, evolving environments.
Salary match Number of applicants Skills match
Your application will include the following questions:
To help fast track investigation, please include here any other relevant details that prompted you to report this job ad as fraudulent / misleading / discriminatory.