Data Scientist

Data Scientist

Date: May 11, 2026 Brand: Luxottica Wholesale North America Location: Dallas, TX, US, 75204 Store #: A00152 Tech Srvcs Wholesale DAL-T Position : Full-Time Total Rewards: Benefits/Incentive Information If you've worn a pair of glasses, we've already met. We are a global leader in the design, manufacture, and distribution of ophthalmic lenses, frames, and sunglasses. We offer our industry stakeholders in over 150 countries access to a global platform of high-quality vision care products such as the Essilor brand, with Varilux, Crizal, Eyezen, Stellest and Transitions, iconic brands that consumers love such as Ray-Ban, Oakley, Persol and Oliver Peoples, as well as a network that offers consumers high-quality vision care and best-in-class shopping experiences such as Sunglass Hut, LensCrafters, and Target Optical, and leading e-commerce platforms. Our portfolio of more than 150 renowned brands span various categories, from frames, lenses and instruments to brick and mortar and digital distribution as well as mid-range to premium segments. Our Shared Services Team, accompany and enable others within the EssilorLuxottica collective to achieve their targets. They keep people and projects running smoothly, ensuring every part of our business is provided for and well taken care of. Join our global community of over 200,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry. This position is not available for visa sponsorship or transfer. Candidates must have authorization to work in the United States. Discover more by following us on LinkedIn!

General Function

The Applied Data Scientist is a business-facing analytics professional responsible for AI research and solution delivery. The role owns the full problem-to-solution journey: engaging stakeholders, translating business challenges into ML solutions, researching emerging AI techniques, and delivering production-ready models that drive measurable business value. It balances innovation with execution, ensuring outcomes are practical, scalable, and aligned to business needs.

Major Duties And Responsibilities

Managing: Engage with stakeholders to understand objectives, constraints, and success metrics Deliver end-to-end ML solutions from problem framing to production deployment Conduct applied research to evaluate and prototype new AI/ML techniques Prioritize between research and delivery based on business impact Manage model lifecycle: validation, monitoring, retraining, and optimization Organizing: Translate ambiguous business problems into structured data science statements Organize exploratory analysis, experimentation, and research frameworks Structure scalable ML pipelines for training, inference, and deployment Develop proof-of-concepts to validate new AI techniques Convert insights and research outcomes into actionable recommendations Leading: Drive solutions from research to production Act as a trusted analytics advisor through consultative problem-solving Promote adoption of ML solutions by communicating value and limitations Evaluate and adopt emerging AI/ML techniques Mentor junior team members on research and delivery best practices

Basic Qualifications

Bachelor's degree in Computer Science, Data Science, Statistics, or related field 5+ years of relevant work experience Proven experience delivering ML solutions in production Ability to work independently with stakeholders and own delivery Strong programming skills in Python or R; experience with ML libraries (scikit-learn, TensorFlow, PyTorch) Solid understanding of statistical modeling, ML techniques, and data mining

Preferred Qualifications

Master's or PhD in Data Science, Statistics, or related discipline Experience in applied research and rapid prototyping of AI/ML solutions Exposure to advanced AI techniques (deep learning, generative AI, optimization) Experience deploying ML models using Docker, Kubernetes, or cloud-native services Familiarity with Azure, Databricks, Spark, Synapse, and Lakehouse architecture Strong storytelling and communication skills for technical and non-technical audiences Experience in agile, fast-paced, cross-functional environments Balance research depth with delivery focus Ability to evaluate and apply new AI techniques pragmatically Excellent listening and problem-solving skills Strong ownership mindset with minimal supervision Ability to translate complex research into business value