Senior Data Scientist

Innowave Tech turns human expertise into intelligent systems that solve real industrial challenges. Headquartered in Singapore, we build AI platforms that help manufacturers inspect quality, understand complex processes and automate equipment operations.

Our work connects artificial intelligence, software and practical engineering with the realities of the factory floor. From detecting subtle defects to helping equipment respond to changing conditions, we turn specialist knowledge into capabilities that people can use, trust and scale.

We’re looking for people who ask thoughtful questions, care about how things work and take pride in making them better. Join us to tackle demanding problems, bring ideas into real operating environments and help shape how industry works next.

Develop deployed AI solutions for manufacturing challenges in collaboration with engineers and domain experts. Lead independent R&D projects to turn solutions into products.

Work with domain experts to define data science problems from business challenges.

Select approaches based on problem context, data availability, and performance constraints.

Source, clean, and validate data.

Build, test, and deploy machine learning models, accounting for assumptions, limitations, and trade-offs.

Contribute to scalable data-processing and model-inference pipelines.

Communicate findings and recommendations to technical and non-technical stakeholders.

Lead independent R&D projects to turn solutions into products.

Diploma, Bachelor's, or Master's degree in Data Science, Statistics, Computer Science, Operations Research, Applied Mathematics, Engineering, or a related field.

At least 5 years of industry experience in data science or applied machine learning, or 3+ years with a PhD.

Track record of taking projects from problem formulation to deployment.

Ability to structure ambiguous requirements with domain experts.

Strong Python skills and hands-on experience with data and machine learning (ML) libraries.

Knowledge of supervised, unsupervised, classical, and modern AI techniques.

Experience in startups or fast-paced, cross-functional teams.

Sensor data, time series analysis, or signal processing.

Optimization algorithms, such as genetic algorithms or Bayesian optimization.

Statistical analysis and modeling.

Retrieval-augmented generation (RAG), agentic AI, harness engineering, or knowledge bases for AI agents.

Semiconductor or high-tech manufacturing background.