Senior Data Science Engineer

About Innowave Tech Singapore

Innowave Tech is an Artificial Intelligence (AI) company offering solutions for the Semiconductor and Advanced Manufacturing industry. Utilizing deep industrial domain knowledge, proven experience, and innovation. We provide expert AI solutions and systems to address various industry pain points.

Roles & Responsibilities

We are seeking a highly skilled Data Scientist with expertise in data analytics, insight generation, and optimization for manufacturing data. The ideal candidate will leverage AI/ML, optimization algorithms, and various advanced analytics techniques to drive operational efficiency and process improvements.

Your Role and Impact

As a Data Scientist, you will beat the forefront of transforming complex domain challenges into impactful AI-driven solutions. You will work side-by-side with engineers, domain experts, and stakeholders to uncover opportunities, shape problem statements, and apply the right data science approaches to solve real-world problems.

You will own the full data science lifecycle, from understanding raw data to deploying robust models, while balancing scientific rigor with practical implementation. Your ability to independently explore data, assess quality, and deliver insights will directly influence decision-making and product development.

Your work will not be isolated to experimentation. You will contribute to solutions and product developments that bridge the gap between theory and implementation. Whether it is predicting equipment health, predicting manufacturing yield, or optimizing processes, your contributions will drive measurable impact in a fast-moving, innovation-driven environment.

What You'll Do

Work closely with domain experts to understand business challenges, formulate problem statements, and translate them into data science solutions.
Evaluate and select appropriate approaches based on problem context, data availability, and performance constraints.
Independently source, clean, and validate data for analysis and modelling, ensuring data quality, consistency, and reliability.
Build, test, and deploy machine learning models with an awareness of model assumptions, limitations, and trade-offs.
Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
Contribute to the design and implementation of robust, scalable pipelines for data processing and model inference.
Meanwhile, you may also lead an independent R&D project to productize our solutions.

Educational Background

Minimum Poly, Bachelor's, or master's degree in data science, Statistics, Computer Science, Operations Research, Applied Mathematics, Engineering, or a related field.

Must-haves

Technical skills:

Minimum 5 years of relevant industry experience in data science or applied machine learning (or 3+ years with a PhD).
Proven ability to take projects from problem formulation to solution deployment.
Proven ability to work with domain experts and turn ambiguous requirements into structured models.
Hands-on experience in data wrangling, cleaning, and validation.
Strong programming skills in Python and familiarity with relevant data/ML libraries.
Proficiency in a wide range of ML techniques (supervised, unsupervised, classical and modern AI) and understanding of model assumptions, limitations, and trade-offs.
Familiarity with startup environments or fast-paced cross-functional teams.

Nice-to-haves

Experience with sensor data, timeseries analysis, or signal processing.
Exposure to optimization algorithms, such as genetic algorithms or Bayesian optimization.
Exposure to statistical analysis and statistical modelling techniques.
Decent understanding of agentic AI, harness engineering, or knowledge base for AI agents.
Background working with semiconductor data or in high-tech manufacturing domains.

Soft Skills

Abilityto work in cross-functional teams and communicate effectively.
Strong problem-solving mindset and attention to detail.
Independent and self-motivated learner who quickly adapts to new domains and technologies and driving the team's technical advancement.