[T04] Machine Learning Engineer (ML) - Permanent Role

TALENTSIS PTE. LTD.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

TALENTSIS PTE. LTD. in Singapore seeks an experienced Senior Data Scientist / Machine Learning Engineer to analyze data, build models and translate business questions into analytics solutions.

You will develop and deploy ML models, collaborate with software and DevOps teams, expose APIs, and monitor performance to drive real-time decisions.

The role requires 3+ years in ML deployment, strong Python/R/SAS skills, SQL, and experience with visualization tools; Scrum experience is a plus.

Qualifications

• Shall have at least three (3) years of implementation and deployment experience in machine learning and statistical data analysis on large datasets.
• Shall have experience in translating business questions into analytical problems and using statistical techniques to derive actionable insights using statistical software (Python, R, SAS), database languages (SQL), visualization tools (Qlik Sense), and deep learning framework (TensorFlow, PyTorch).
• Shall have the following skillsets: Data Wrangling, Data Visualization, DBMS, SQL, Python
• Preferably have completed at least one (1) project using Scrum or equivalent Agile development framework.
• Experience in these products/tools would be advantageous: MS Access (Front-end), SharePoint (Back-end), Power BI (Visualisation), Kubernetes, Docker/Podman, Microservices Architecture, DevSecOps Methodology

Responsibilities

• Data analysis and insights: Perform exploratory data analysis, generate insights, and present findings to stakeholders. Use statistical methods and visualization techniques to communicate complex concepts and patterns effectively.
• Develop and deploy machine learning models: Design, build, and optimize machine learning models and algorithms to solve specific business problems. Collaborate with cross-functional teams to gather requirements, define objectives, and deploy models into production environments.
• Model training and evaluation: Train and fine-tune machine learning models using appropriate algorithms and techniques. Evaluate model performance and identify areas for improvement, employing techniques such as cross-validation, hyper parameter optimization, and ensemble methods.
• Model deployment and integration: Collaborate with software engineers and DevOps teams to deploy machine learning models into production environments. Implement APIs and integrate models with existing systems and applications to enable real-time decision-making.
• Performance monitoring and maintenance: Monitor model performance and address any issues or anomalies that arise. Continuously improve models by refining algorithms, optimizing code, and incorporating feedback from users and stakeholders.
• Stay up-to-date with the latest advancements: Keep abreast of the latest research and trends in machine learning and artificial intelligence. Evaluate and recommend new tools, libraries, and methodologies to enhance the efficiency and effectiveness of the machine learning workflow.

Skills

Data Wrangling

Data Visualization

DBMS

SQL

Python

Tools

Kubernetes

Docker/Podman

Power BI

Qlik Sense

TensorFlow

PyTorch

Microservices Architecture

DevSecOps

MS Access

SharePoint

Job description

We are seeking an experienced Senior Data Scientist / Machine Learning Engineer to join our dynamic team. As a Senior Data Scientist / Machine Learning Engineer, you will play a key role in analyzing and presenting data, developing and implementing machine learning models and algorithms to solve complex business problems. Your expertise will contribute to enhancing the delivery service of analytics solutions and products to our customers.

Job Responsibilities

• Data analysis and insights: Perform exploratory data analysis, generate insights, and present findings to stakeholders. Use statistical methods and visualization techniques to communicate complex concepts and patterns effectively.
• Develop and deploy machine learning models: Design, build, and optimize machine learning models and algorithms to solve specific business problems. Collaborate with cross-functional teams to gather requirements, define objectives, and deploy models into production environments.
• Model training and evaluation: Train and fine-tune machine learning models using appropriate algorithms and techniques. Evaluate model performance and identify areas for improvement, employing techniques such as cross-validation, hyper parameter optimization, and ensemble methods.
• Model deployment and integration: Collaborate with software engineers and DevOps teams to deploy machine learning models into production environments. Implement APIs and integrate models with existing systems and applications to enable real-time decision-making.
• Performance monitoring and maintenance: Monitor model performance and address any issues or anomalies that arise. Continuously improve models by refining algorithms, optimizing code, and incorporating feedback from users and stakeholders.
• Stay up-to-date with the latest advancements: Keep abreast of the latest research and trends in machine learning and artificial intelligence. Evaluate and recommend new tools, libraries, and methodologies to enhance the efficiency and effectiveness of the machine learning workflow.

Job Requirements

• Shall have at least three (3) years of implementation and deployment experience in machine learning and statistical data analysis on large datasets.
• Shall have experience in translating business questions into analytical problems and using statistical techniques to derive actional insights using statistical software (Python, R, SAS), database languages (SQL), visualization tools (Qlik Sense), and deep learning framework (TensorFlow, PyTorch).
• Shall have the following skillsets: Data Wrangling, Data Visualization, DBMS, SQL, Python
• Preferably have completed at least one (1) project using Scrum or equivalent Agile development framework.
• Experience in these products/tools would be advantageous: MS Access (Front-end), SharePoint (Back-end), Power BI (Visualisation), Kubernetes, Docker/Podman, Microservices Architecture, DevSecOps Methodology

(EA Reg No: 20C0312)

Only shortlisted candidates will be notified.