Senior Data Science Engineer

Innowave Tech Pte Ltd; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Innowave Tech Pte Ltd, headquartered in Singapore, seeks an experienced data scientist to turn manufacturing data into AI-driven solutions. You will own the data science lifecycle from data assessment to model deployment while collaborating with engineers and domain experts.

Responsibilities include translating business challenges, building robust models, validating data, and communicating insights to stakeholders. Prior experience in manufacturing AI is valued.

Qualifications

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

• Work with domain experts to translate manufacturing challenges into data science solutions.
• Define problems and translate business challenges into data science solutions.
• Source, clean, and validate data for analysis and modeling.
• Build, test, and deploy machine learning models with consideration of assumptions and trade-offs.
• Communicate findings to technical and nontechnical stakeholders.
• Design scalable data-processing and model-inference pipelines.
• Lead an independent R&D project to productize solutions.

Skills

Python programming

Data wrangling

Model deployment

Stakeholder communication

Problem formulation

Cross-functional collaboration

Education

Bachelor's or Master's in Data Science/ CS/ OR/ Math

Tools

Pandas

NumPy

scikit-learn

TensorFlow/PyTorch

Job description

Build and deploy data science solutions for manufacturing data, from data preparation and modeling to scalable pipelines and stakeholder recommendations. Projects may include equipment health prediction, manufacturing yield prediction, and process optimization.

Experience (years) 5+

About Innowave Tech

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.

About the role

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.

Key responsibilities

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.

Required skills and experience

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.

Good to have

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.