Machine Learning Lead

About Avo Intelligence

Avo Intelligence operates at the forefront of machine learning innovation, delivering transformative solutions that empower businesses to unlock the potential of their data. Based in Singapore, we specialize in providing world-class machine learning expertise that drives impactful results across industries such as finance, healthcare, logistics, and smart urban planning. With a network of highly skilled professionals experienced in raw machine learning applications, we are dedicated to fostering innovation and delivering meaningful outcomes for our clients.

About The Role

We are seeking a passionate and experienced Machine Learning Lead to spearhead the development and optimization of algorithms that drive innovative machine learning solutions across diverse projects. This role involves taking ownership of the end-to-end ML lifecycle—from data exploration and model development to deployment—while mentoring a team of talented professionals. If you thrive on solving complex challenges, driving impactful innovation, and shaping the future of machine learning, we’d love to have you on board.

Tasks

1. Collaborate with stakeholders to understand business objectives and design machine learning models to achieve them, including defining metrics to measure success.
2. Lead and oversee resource allocation (hardware, data, personnel) to ensure project deadlines are consistently met.
3. Analyze and evaluate machine learning algorithms, ranking their effectiveness and selecting the best approaches for real-world deployment.
4. Explore and visualize data to gain actionable insights, identifying and mitigating issues in data distribution that could affect model performance.
5. Ensure data quality through cleaning, augmentation, and preprocessing, while also supervising the data acquisition process when needed.
6. Identify and leverage publicly available datasets for training and develop custom feature engineering and data augmentation pipelines.
7. Develop and implement validation strategies to ensure robust model performance.
8. Train, fine-tune, and optimize ML models, including hyperparameter tuning and performance evaluation.
9. Strategize to overcome model errors and design fail-safe deployment strategies for production environments.
10. Lead the integration and deployment of ML models into production systems, ensuring scalability and efficiency.
11. Guide the team in leveraging the GCP Ecosystem and other cloud platforms for ML workflows.

Requirements

1. Bachelor’s, Master’s, or Ph.D. in a quantitative field such as Computer Science, Electrical Engineering, Information Sciences, Statistics, Mathematics, Economics, or Operations Research, with a focus on applied and foundational Machine Learning, Artificial Intelligence, or statistical modeling.
2. At least 4+ years of hands-on experience applying AI/ML, NLP, deep learning, and statistical analysis to solve real-world challenges across multiple domains.
3. Strong programming expertise, with proven experience in machine learning frameworks such as TensorFlow, PyTorch, or similar, and big data technologies such as Spark and Hadoop.
4. Demonstrated ability to customize and extend open-source ML frameworks, developing tailored algorithms and solutions.
5. Proven track record of delivering successful technology solutions, including prototypes, proof-of-concepts (POCs), well-cited research publications, and/or deployed products.
6. Expertise in data management, analytics platforms, cloud infrastructure (e.g., GCP, AWS, Azure), and scalable ML systems is a significant advantage.
7. Experience in mentoring and guiding cross-functional teams, with a strong ability to communicate complex concepts to stakeholders across technical and non-technical disciplines.
8. Exceptional analytical and problem-solving skills, coupled with a proactive and collaborative approach to leadership.

Benefits

1. Work with some of the brightest minds in the machine learning field, tackling industry-specific challenges and pushing the boundaries of innovation.
2. Collaborate on diverse projects that span industries and make a tangible impact.
3. Be part of a global network of ML experts who are passionate about solving real-world problems through raw machine learning applications.