Data Scientist

Sephora-posted 3 months ago

San Francisco, CA

5,001-10,000 employees

Health and Personal Care Retailers

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As a Data Scientist at Sephora, you will lead the development of ML systems, drive innovation through cutting-edge AI solutions, and collaborate with cross-functional teams to deliver actionable insights. This individual will apply expertise in Generative AI to a variety of use cases, driving customer and employee experience. This role presents an exciting opportunity to shape our data-driven strategies and contribute to developing state-of-the-art solutions.

Conduct exploratory data analysis, data preprocessing, and feature engineering to ensure high-quality input for modeling.
Lead the design, development, and deployment of advanced machine learning models and algorithms to solve complex business challenges.
Utilize your strong understanding of Recommendation Systems to design personalized user experiences, drive engagement, and optimize content delivery.
Stay at the forefront of AI research, experimenting with emerging techniques and staying informed about the latest advancements in the field.
Communicate findings, insights, and technical concepts to both technical and non-technical audiences through clear visualizations and presentations.
Contribute to developing and maintaining robust machine learning best practices within the organization.
Develop models contributing to synthetic data generation and data augmentation efforts.

4+ years experience in data science and machine learning, demonstrating expertise in areas such as Generative AI, Recommendation Systems, and Computer Vision.
4+ years proficiency in programming languages such as Python, and hands-on experience with machine learning libraries and frameworks.
Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and hands-on experience in implementing complex AI models.
Proven track record of building Recommendation Systems and expertise in designing and evaluating personalized content delivery strategies.
Experience with Computer Vision tasks, such as image classification, object detection, image segmentation, and familiarity with related libraries.
Experience working with cloud computing platforms (AWS, Azure, GCP) and distributed computing is a plus.
Machine Learning for Recommendations - Experience with collaborative filtering, content-based filtering, ranking models, and hybrid approaches.
Feature Engineering - Ability to extract, transform, and create features from structured and unstructured data.
Model Development & Evaluation - Proficiency in designing, training, and evaluating ML models using metrics relevant to recommendations (e.g., MAP, NDCG, CTR, CVR).
Python & ML Libraries - Strong skills in Python and libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
Data Processing at Scale - Experience with PySpark or distributed computing frameworks for handling large datasets.
SQL & Data Querying - Advanced SQL skills for extracting and joining data from multiple sources.
Experimentation - Knowledge of A/B testing design, statistical analysis, and interpreting experimental results.
Model Deployment & MLOps - Familiarity with deploying models to production and monitoring their performance.
Version Control & Collaboration - Experience with Git and working in collaborative, cross-functional environments.
Excellent problem-solving skills, analytical thinking, and the ability to approach complex challenges creatively.
Strong verbal and written communication skills to effectively collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders.

Experience working with cloud computing platforms (AWS, Azure, GCP) and distributed computing.

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