Wayfair - Machine Learning Scientist III in Bangalore, भारत

Who we are : Wayfair is moving the world so that anyone can live in a home they love. This journey is enabled by more than 3,000 Wayfair engineers and a data-centric https://jobeax.com/link/0ZBqRz1BTbOdFtQX Customer Technology (CT) Science team builds and owns machine learning products that power search, marketing, and recommendations across Wayfair. Our algorithms address a broad range of challenges in the Wayfair marketplace, from helping suppliers add products to our catalog to enabling customers to discover and purchase a vast assortment of home goods and delivering marketing experiences customers love, all at web https://jobeax.com/link/Qppr0WvBdKijniX6 are looking for an experienced Machine Learning Scientist to develop the next generation of personalization and customer understanding systems at Wayfair. You will build large-scale models that learn from billions of customer interactions to improve content recommendations, personalized search, and product discovery. You will also create reusable customer representations that support experiences across the Wayfair https://jobeax.com/link/Ijt9JG5TifItzKJ5 this role, you will own machine learning solutions end to end, from problem definition and hypothesis testing to model development, evaluation, and production deployment. You will partner closely with engineers, product managers, platform teams, and scientists to solve complex challenges in representation learning, ranking, retrieval, and personalization. Your work will translate advances in machine learning research into scalable capabilities used across search, recommendations, marketing, and other customer-facing products, improving relevance and discovery for millions of https://jobeax.com/link/NdSfgOpymoK7swUu you'll do : - Design, train, evaluate, and deploy large-scale machine learning models for customer understanding, personalization, search, and recommendations.- Evaluate and adapt advances in recommendation systems, representation learning, large language models, and foundation models to solve practical customer and business problems.- Develop rigorous offline evaluation frameworks and design online A/B tests to measure customer and business impact.- Collaborate with product, engineering, and analytics teams to translate ambiguous customer needs into well-defined machine learning problems.- Partner with engineers to productionize models and build reliable, scalable training and inference pipelines.- Monitor model performance, identify opportunities for improvement, and continuously iterate on models in https://jobeax.com/link/QSlET2skWZuTD02l you'll need : - A bachelors degree in Computer Science, Mathematics, Statistics, or a related field with 7+ years of relevant industry experience; or a masters degree in a related field with 6+ years of relevant industry experience.- Proven experience building and deploying machine learning models in production at scale.- Strong knowledge of machine learning, statistics, optimization, and modern deep learning techniques.- Experience designing, training, and evaluating models using large-scale datasets.- Proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.- Strong software engineering fundamentals and experience partnering with engineering teams to build reliable, scalable ML systems.- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to a range of audiences.- Intellectual curiosity and a commitment to continuous https://jobeax.com/link/2SEi4L3nTx9a0jku to have : - Experience building recommender systems, personalization models, retrieval and ranking systems, or customer understanding models.- Experience with representation learning, embeddings, contrastive learning, sequence modeling, or transformer architectures.- Experience with distributed model training and large-scale retrieval and inference pipelines.- Experience with cloud platforms such as GCP, AWS, or Azure.- Familiarity with MLOps and workflow orchestration tools such as Airflow, Kubeflow, MLflow, or Vertex AI.- Experience applying large language models or generative AI techniques to personalization, search, or recommendation problems. (ref:hirist.tech)