Responsibilities :

• 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, hyperparameter 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.
• 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.
• 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.

REQUIREMENTS:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. An equivalent combination of education and experience will also be considered.
• Experience: At least 2 years’ experience working in a similar role. Hands-on experience in designing, developing, and deploying machine learning models in real-world applications.
• Solid understanding of machine learning algorithms, techniques, and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with working on Large Language Models and Generative AI technology is a plus.

TECHNICAL SKILLS:

• Strong programming skills in languages such as Python.
• Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn etc.
• Solid understanding of Statistical Analysis, Probability Theory, and Hypothesis Testing.
• Familiarity with machine learning tools on cloud platforms (e.g., AWS, Azure, GCP) and distributed computing frameworks (e.g., Spark) is a plus.
• Problem-solving and analytical mindset: Ability to analyze complex problems, break them down into solvable components, and develop innovative machine learning solutions. Strong mathematical and analytical skills are essential.
• Communication and collaboration: Excellent verbal and written communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders. Proven ability to work collaboratively in a team environment and effectively manage multiple priorities.
• Adaptability and continuous learning: Willingness to adapt to evolving technologies and learn new tools and techniques. Demonstrated commitment to staying updated with the latest advancements in machine learning and artificial intelligence.
  Only shortlisted candidates will be notified.
  Please email a copy of your detailed resume to chally@talentsis.com.sg for immediate processing.
  (EA Reg No: 20C0312

• 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, hyperparameter 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.
• 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.
• 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