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 180,000 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:
We are seeking a talented and experienced Data Scientist with a strong focus on machine learning in production. In this role, you will be responsible for developing, deploying, and maintaining machine learning models that power critical business applications. You will collaborate with cross-functional teams, including data engineers, software engineers, and product managers, to ensure the successful integration of ML models into production systems. Your expertise in building scalable, robust, and efficient ML solutions will be instrumental in delivering value through data-driven insights.
- AREAS OF RESPONSIBILITIES AND RELATED ACTIVITIES:
- Develop and implement machine learning models for various business use cases, focusing on their successful deployment and integration into production systems.
- Collaborate with data engineers and software engineers to design and implement scalable data pipelines, ensuring the smooth flow of data from ingestion to model training and inference.
- Conduct exploratory data analysis to identify patterns, trends, and potential insights that can be leveraged for ML model development and improvement.
- Fine-tune and optimize ML models for performance, scalability, and accuracy, considering factors such as feature engineering, model selection, hyperparameter tuning, and ensemble methods.
- Deploy ML models into production environments, working closely with DevOps teams to ensure reliability, scalability, and monitoring of model performance.
- Conduct rigorous testing and validation of ML models, ensuring their robustness and stability in real-world scenarios.
- Monitor and analyze model performance, identifying areas for improvement and proactively making adjustments as needed.
- Stay up to date with the latest advancements in machine learning, data science, and related technologies, and assess their potential application to enhance existing systems
NETWORK OF INTERACTION:
INTERNAL : Business Units
EXTERNAL : MS, Google, Amazon etc
- TECHNICAL SKILLS - PORTRAIT OF A PERFECT CANDIDATE
- Bachelor's degree in Computer Science, Data Science, Statistics, or a related field. A master's or Ph.D. in a relevant discipline is a plus.
- Proven experience as a data scientist, with a focus on machine learning in a production environment.
- Strong programming skills in languages such as Python or R, with experience using ML libraries and frameworks like TensorFlow, PyTorch, or scikit-learn.
- Solid understanding of statistical modeling, data mining, and machine learning techniques, including regression, classification, clustering, and deep learning.
- Experience with deploying ML models in production, using frameworks like TensorFlow Serving, Kubernetes, or Docker.
- Proficiency in working with large datasets and data preprocessing techniques.
- Strong problem-solving skills, with the ability to translate business objectives into ML solutions.
- Excellent communication skills, with the ability to effectively convey complex concepts to technical and non-technical stakeholders.
- Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Hadoop, Spark) is a plus.
- Experience with version control systems (e.g., Git) and agile development methodologies is advantageous.
- Strong customer engagement skills to understand customer needs for Analytics solutions. Experience in working with small and large teams in delivering analytics solutions for customers Have demonstrated ability to define, develop and implement data models and supporting policies, standards, and guidelines.
- Strong data analysis and analytical skills.
- Have demonstrated the ability to guide the development of data requirements for projects and drive a user experience that is easy to use and delivers the right information at the right time.
- Experience in working in a fast-paced agile environment.
- Strong problem solving and troubleshooting skills.