Principal Data scientistEngineer

Singapore - Singapore

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

We are seeking an exceptionally talented and innovative Machine Learning Scientist/Engineer to join our clients advanced AI/ML research and development this role you will be at the forefront of designing developing and deploying state-of-the-art machine learning algorithms and models that directly impact our core products and user experiences. Youll blend theoretical expertise with practical engineering skills to tackle challenges that have never been solved before working with massive datasets and distributed systems.

What Youll Do:

Research & Innovate: Identify research and develop novel machine learning algorithms and models (e.g. deep learning reinforcement learning causal inference) to address critical product challenges and unlock new capabilities.
Model Development & Deployment: Design train evaluate and deploy highly scalable and efficient ML models into production systems ensuring robustness performance and reliability.
Feature Engineering & Data Understanding: Collaborate with data engineers and data scientists to identify create and refine features from vast and diverse datasets to optimize model performance.
Experimentation & Evaluation: Establish rigorous evaluation methodologies for new models conduct A/B tests analyze results and iterate on solutions based on empirical evidence.
Cross-functional Collaboration: Work closely with product managers software engineers and other research scientists to translate business problems into ML solutions and integrate these solutions into products.
Stay Cutting-Edge: Keep abreast of the latest advancements in academic and industry ML research contributing to publications and patents where appropriate and applying relevant techniques to our challenges.
Drive Best Practices: Champion best practices in ML model development MLOps reproducibility and ethical AI across the organization.

What Youll Bring:

Ph.D. or Masters degree in Computer Science Machine Learning Statistics or a closely related quantitative field with a strong publication record in top-tier conferences (e.g. NeurIPS ICML ICLR KDD) or equivalent industry experience.
5 years of experience in applied machine learning research and development building and deploying ML systems at scale.
Expert proficiency in Python and popular ML frameworks (e.g. TensorFlow PyTorch JAX).
Solid understanding of core ML concepts: supervised/unsupervised learning reinforcement learning deep learning architectures (e.g. Transformers CNNs RNNs) Bayesian methods optimization techniques and feature engineering.
Experience with large-scale data processing: Proficiency in SQL and experience with big data technologies (e.g. Spark Hadoop) is essential.
Strong experimental design skills: Ability to set up and analyze A/B tests understand statistical significance and interpret results in a product context.
Excellent problem-solving skills: Ability to break down complex problems identify root causes and develop innovative solutions.
Exceptional communication skills: Ability to clearly articulate complex technical concepts and research findings to both technical and non-technical audiences.
Passion for impact: A desire to see your research and models directly improve user experiences for millions/billions.

What Youll Do:

Research & Innovate: Identify research and develop novel machine learning algorithms and models (e.g. deep learning reinforcement learning causal inference) to address critical product challenges and unlock new capabilities.
Model Development & Deployment: Design train evaluate and deploy highly scalable and efficient ML models into production systems ensuring robustness performance and reliability.
Feature Engineering & Data Understanding: Collaborate with data engineers and data scientists to identify create and refine features from vast and diverse datasets to optimize model performance.
Experimentation & Evaluation: Establish rigorous evaluation methodologies for new models conduct A/B tests analyze results and iterate on solutions based on empirical evidence.
Cross-functional Collaboration: Work closely with product managers software engineers and other research scientists to translate business problems into ML solutions and integrate these solutions into products.
Stay Cutting-Edge: Keep abreast of the latest advancements in academic and industry ML research contributing to publications and patents where appropriate and applying relevant techniques to our challenges.
Drive Best Practices: Champion best practices in ML model development MLOps reproducibility and ethical AI across the organization.

What Youll Bring:

Ph.D. or Masters degree in Computer Science Machine Learning Statistics or a closely related quantitative field with a strong publication record in top-tier conferences (e.g. NeurIPS ICML ICLR KDD) or equivalent industry experience.
5 years of experience in applied machine learning research and development building and deploying ML systems at scale.
Expert proficiency in Python and popular ML frameworks (e.g. TensorFlow PyTorch JAX).
Solid understanding of core ML concepts: supervised/unsupervised learning reinforcement learning deep learning architectures (e.g. Transformers CNNs RNNs) Bayesian methods optimization techniques and feature engineering.
Experience with large-scale data processing: Proficiency in SQL and experience with big data technologies (e.g. Spark Hadoop) is essential.
Strong experimental design skills: Ability to set up and analyze A/B tests understand statistical significance and interpret results in a product context.
Excellent problem-solving skills: Ability to break down complex problems identify root causes and develop innovative solutions.
Exceptional communication skills: Ability to clearly articulate complex technical concepts and research findings to both technical and non-technical audiences.
Passion for impact: A desire to see your research and models directly improve user experiences for millions/billions.