Singapore, SG
Live, first seen by us 19 days agoCurrently pursuing a Master's degree in Big data engineering, data science, computer science, biostatistics & biomedical data, or a related discipline
Develop and evaluate models predicting myopia progression and treatment outcomes
Run data quality checks, exploratory analysis and data preparation
Validate models through cross-validation and external validation
Document methods, scripts and results for reproducible research
Present model performance and findings to the project team
Develop and evaluate statistical and machine-learning models for predicting myopia progression and treatment outcomes
data quality checks and exploratory data analysis, data preparation
Perform cross-validation and/or external validation where appropriate.
Document data-processing procedures, modelling methodologies, assumptions, parameters, and validation approaches.
Present analytical findings and model performance to the project team.
Currently pursuing a Master's degree in Big data engineering, data science, computer science, biostatistics & biomedical data, or a related discipline.
Able to commit for a minimum of 6 Months (Jan - Jun/Jul, 2027)
Proficiency in R and/or Python for data analysis and statistical/machine-learning modelling.
Familiarity with data manipulation, visualization, statistical modelling, and machine-learning methods.
KEY REQUIREMENTS: - Currently pursuing a Master's degree in Big data engineering, data science, computer science, biostatistics & biomedical data, or a related discipline. - Able to commit for a minimum of 6 Months (Jan - Jun/Jul, 2027)
We are EssilorLuxottica, a global leader in the design, manufacture and distribution of ophthalmic lenses, frames and sunglasses.
In the employer’s words · Read in context
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 19
Similar jobs usually come down between day 6 and day 70.
At EssilorLuxottica Group, jobs usually come down by about day 51.
From 4,461 similar postings that came down, and 24 of EssilorLuxottica Group’s. Coming down is not always a hire.
12 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Able to commit for a minimum of 6 Months (Jan - Jun/Jul, 2027) - Proficiency in R and/or Python for data analysis and statistical/machine-learning modelling. - Familiarity with data manipulation, visualization, statistical modelling, and machine-learning methods.
- Familiarity with data manipulation, visualization, statistical modelling, and machine-learning methods. - Experience with packages such as tidyverse, ggplot2, tidymodels/caret (R) or pandas, NumPy, scikit-learn, and matplotlib (Python) is preferred. - Familiarity with machine-learning methods such as linear and generalized linear models, regularization, tree-based methods, random forests, gradient boosting, and model validation is an advantage.
Complete your application on careers.essilorluxottica.com. The employer’s form will show what is required.
Already applied? Track this application
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
WHO WE ARE
We are EssilorLuxottica, a global leader in the design, manufacture and distribution of ophthalmic lenses, frames and sunglasses. The Company brings together the complementary expertise of two industry pioneers, one in advanced lens technologies and the other in the craftsmanship of iconic eyewear, to create a vertically integrated business that is uniquely positioned to address the world’s evolving vision needs and the global demand of a growing eyewear industry.
With over 200,000 dedicated employees in 150 countries driving our iconic brands, our people are creative, entrepreneurial and celebrated for their unique perspectives and individuality. Committed to vision, we enable people to “see more and be more” thanks to our innovative designs and lens technologies, exceptional quality and cutting-edge processing methods. Every day we impact the lives of millions by changing the way people see the world.
JOB SCOPE AND MAIN RESPONSIBILITIES:
Your mission will be to support the Applied Myopia Research R&D team in developing and validating prediction models for myopia progression and treatment outcomes, preferably using machine learning techniques within the Applied Myopia Research domain.
You will be involved in data quality checks and exploratory data analysis, data preparation, development and testing of machine‑learning–based prediction models, and monitoring model performance using appropriate validation metrics. You will also participate in documenting methodologies and results, and in communication of findings and technical discussions with the project team.
The expected results include reliable data models, well-documented methods, and clear summaries of findings. The main challenges would be learning and applying appropriate machine learning techniques for prediction models for myopia progression and control, and managing project timelines. Success requires scientific curiosity, attention to detail, initiative, and effective teamwork.
AREAS OF RESPONSIBILITIES AND RELATED ACTIVITIES:
Prediction Model Development and Evaluation
Develop and evaluate statistical and machine-learning models for predicting myopia progression and treatment outcomes.
Apply appropriate supervised learning techniques to longitudinal ophthalmic and clinical datasets.
Compare different modelling approaches and identify suitable models for specific prediction objectives.
Tune model parameters and optimize model performance while considering interpretability and clinical relevance.
Evaluate model performance using appropriate metrics, such as RMSE, MAE, R², calibration, discrimination, and prediction error, as appropriate to the modelling task.
Contribute to the development of reproducible modelling pipelines.
Model Validation and Performance Monitoring
Implement appropriate training, validation, and testing strategies for predictive models.
Perform cross-validation and/or external validation where appropriate.
Assess model generalizability, robustness, calibration, and potential overfitting.
Compare model predictions against observed longitudinal outcomes.
Assist in monitoring model performance across relevant subgroups, such as different ages, baseline refractive errors, or study populations.
Documentation and Reproducible Research
Document data-processing procedures, modelling methodologies, assumptions, parameters, and validation approaches.
Maintain well-organized and reproducible analysis scripts and workflows.
Prepare tables, figures, technical summaries, and other analysis outputs.
Contribute to internal technical reports, presentations, and potentially scientific publications or conference materials.
Scientific Analysis and Interpretation
Interpret modelling results in collaboration with scientists and researchers in the Applied Myopia Research R&D team.
Relate statistical and machine-learning findings to the biological and clinical aspects of myopia progression.
Identify limitations, assumptions, and potential sources of bias in prediction models.
Contribute to scientific discussions regarding model development and application.
Communication and Team Collaboration
Present analytical findings and model performance to the project team.
Communicate technical concepts and results clearly to colleagues with different levels of data-science expertise.
Participate in technical discussions and contribute ideas for improving modelling approaches.
Work collaboratively with data scientists, clinicians, researchers, and other R&D team members.
KEY REQUIREMENTS:
Currently pursuing a Master's degree in Big data engineering, data science, computer science, biostatistics & biomedical data, or a related discipline.
Proficiency in R and/or Python for data analysis and statistical/machine-learning modelling.
Familiarity with data manipulation, visualization, statistical modelling, and machine-learning methods.
Experience with packages such as tidyverse, ggplot2, tidymodels/caret (R) or pandas, NumPy, scikit-learn, and matplotlib (Python) is preferred.
Familiarity with machine-learning methods such as linear and generalized linear models, regularization, tree-based methods, random forests, gradient boosting, and model validation is an advantage.
Experience with Git/GitHub, R Markdown/Quarto, or Jupyter for reproducible analysis is an advantage.
Experience or interest in longitudinal data analysis, predictive modelling, and/or biomedical data science is highly desirable. Knowledge of mixed-effects models, repeated-measures data, or time-dependent prediction is an advantage.
Reliable, autonomous, precise, good organization & communication skills
Positive and proactive attitude
Recruiting process
Our recruitment process may vary; If you are selected, you will be contacted by our recruiters to guide you through the specific steps for your application.
Our Diversity, Equity and Inclusion commitment
We are committed to creating an inclusive environment for all employees. We celebrate diversity and provide equal opportunities to all, regardless of race, gender, ethnicity, religion, disability, sexual orientation, or any other characteristic that makes us unique.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Singapore, SG
Working pattern and location restrictions need checking in the full posting.
No clear work-authorization passage found. Eligibility is unconfirmed.