{"id":2698399,"url":"https://alion.io/job/essilorluxottica-vision-science-machine-learning-research-intern","title":"Vision Science & Machine Learning Research Intern","company":{"id":1782239,"name":"EssilorLuxottica","domain":"essilorluxottica.com","url":"https://alion.io/company/essilorluxottica","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":100,"open_postings":6,"ghost_share":0,"stale_share":0.167,"repost_share":0,"time_to_fill_p50_days":10,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ggplot2","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"tidymodels","optional":false},{"name":"R","optional":true}],"status":"live","first_seen_at":"2026-10-16T10:40:46Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-24T11:23:00Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"WHO WE ARE\nWe 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.\nWith 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.\nJOB SCOPE AND MAIN RESPONSIBILITIES:\nYour 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.\nYou 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.\nThe 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.\nAREAS OF RESPONSIBILITIES AND RELATED ACTIVITIES:\nPrediction Model Development and Evaluation\nDevelop and evaluate statistical and machine-learning models for predicting myopia progression and treatment outcomes.\n\nApply appropriate supervised learning techniques to longitudinal ophthalmic and clinical datasets.\n\nCompare different modelling approaches and identify suitable models for specific prediction objectives.\n\nTune model parameters and optimize model performance while considering interpretability and clinical relevance.\n\nEvaluate model performance using appropriate metrics, such as RMSE, MAE, R², calibration, discrimination, and prediction error, as appropriate to the modelling task.\n\nContribute to the development of reproducible modelling pipelines.\n\nModel Validation and Performance Monitoring\nImplement appropriate training, validation, and testing strategies for predictive models.\n\nPerform cross-validation and/or external validation where appropriate.\n\nAssess model generalizability, robustness, calibration, and potential overfitting.\n\nCompare model predictions against observed longitudinal outcomes.\n\nAssist in monitoring model performance across relevant subgroups, such as different ages, baseline refractive errors, or study populations.\n\nDocumentation and Reproducible Research\nDocument data-processing procedures, modelling methodologies, assumptions, parameters, and validation approaches.\n\nMaintain well-organized and reproducible analysis scripts and workflows.\n\nPrepare tables, figures, technical summaries, and other analysis outputs.\n\nContribute to internal technical reports, presentations, and potentially scientific publications or conference materials.\n\nScientific Analysis and Interpretation\nInterpret modelling results in collaboration with scientists and researchers in the Applied Myopia Research R&D team.\n\nRelate statistical and machine-learning findings to the biological and clinical aspects of myopia progression.\n\nIdentify limitations, assumptions, and potential sources of bias in prediction models.\n\nContribute to scientific discussions regarding model development and application.\n\nCommunication and Team Collaboration\nPresent analytical findings and model performance to the project team.\n\nCommunicate technical concepts and results clearly to colleagues with different levels of data-science expertise.\n\nParticipate in technical discussions and contribute ideas for improving modelling approaches.\n\nWork collaboratively with data scientists, clinicians, researchers, and other R&D team members.\n\nKEY REQUIREMENTS:\nCurrently pursuing a Master's degree in Big data engineering, data science, computer science, biostatistics & biomedical data, or a related discipline.\n\nAble to commit for a minimum of 6 Months (Jan - Jun/Jul, 2027)\nProficiency in R and/or Python for data analysis and statistical/machine-learning modelling.\n\nFamiliarity with data manipulation, visualization, statistical modelling, and machine-learning methods.\n\nExperience with packages such as tidyverse, ggplot2, tidymodels/caret (R) or pandas, NumPy, scikit-learn, and matplotlib (Python) is preferred.\n\nFamiliarity 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.\n\nExperience with Git/GitHub, R Markdown/Quarto, or Jupyter for reproducible analysis is an advantage.\n\nExperience 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.\n\nReliable, autonomous, precise, good organization & communication skills\n\nPositive and proactive attitude\n\nRecruiting process\nOur 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.\nOur Diversity, Equity and Inclusion commitment\nWe 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.","description_format":"text","description_chars":6242,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Eyewear","Optometry & Optical Stores"],"lifecycle":[{"event":"open","at":"2026-10-16T10:40:46Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.834,"p_room":0.75,"age_days":7,"expected_fill_days":10,"reasons":["conf:4","velocity","win:late","comp:junior,brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/essilorluxottica-vision-science-machine-learning-research-intern","json_url":"https://alion.io/job/essilorluxottica-vision-science-machine-learning-research-intern.json","meta":{"generated_at":"2026-10-24T19:27:48Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":28517,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1782239},"rest":"https://alion.io/mcp/rest/get_company?id=1782239"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fessilorluxottica-vision-science-machine-learning-research-intern"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fessilorluxottica-vision-science-machine-learning-research-intern"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fessilorluxottica-vision-science-machine-learning-research-intern"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/essilorluxottica-vision-science-machine-learning-research-intern\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fessilorluxottica-vision-science-machine-learning-research-intern"}]}