Senior Data Science Engineer

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.

Work with engineers, domain experts, and stakeholders to turn manufacturing challenges into AI-driven solutions. Own the data science lifecycle from raw data assessment through model deployment and contribute to product development.

Work with domain experts to define problems and translate business challenges into data science solutions.

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

Source, clean, and validate data for analysis and modeling.

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

Communicate findings and recommendations to technical and nontechnical stakeholders.

Help design and implement scalable data-processing and model-inference pipelines.

May lead an independent R&D project to productize solutions.

Polytechnic diploma, bachelor's degree, or master's degree in Data Science, Statistics, Computer Science, Operations Research, Applied Mathematics, Engineering, or a related field.

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

Experience taking projects from problem formulation through solution deployment.

Ability to work with domain experts and turn ambiguous requirements into structured models.

Hands-on experience in data wrangling, cleaning, and validation.

Strong Python programming skills and familiarity with relevant data and machine learning libraries.

Proficiency in supervised and unsupervised machine learning, including classical and modern AI techniques, and understanding of model assumptions, limitations, and trade-offs.

Familiarity with startup environments or fast-paced cross-functional teams.

Cross-functional communication, problem-solving, attention to detail, independent learning, and adaptability.

Experience with sensor data, time series analysis, or signal processing.

Exposure to optimization algorithms, such as genetic algorithms or Bayesian optimization.

Exposure to statistical analysis and statistical modeling.

Understanding of agentic AI, harness engineering, or knowledge bases for AI agents.

Experience with semiconductor data or high-tech manufacturing.