CA, US
What you'll do:
- Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirements and translate them into machine learning solutions.
- Deploy and monitor machine learning and optimization models to solve complex retail problems such as customer segmentation, recommendation systems, personalized marketing, and supply chain.
- Implement best practices of MLOps to ensure product health
- Apply state-of-the-art techniques in deep learning, natural language processing, reinforcement learning, and computer vision to drive innovation and improve business performance.
- Design and implement scalable and efficient algorithms to process large-scale datasets, ensuring robustness, accuracy, and scalability.
- Analyze and interpret the results of ML models, providing actionable insights and recommendations to stakeholders.
- Collaborate with data engineers to ensure the availability, quality, and reliability of data required for machine learning models.
- Collaborate with Developer AI Platform teams to enable delivery of new Generative AI capabilities.
- Stay up-to-date with the latest advancements in machine learning and retail industry trends, proactively identifying opportunities for improvement and innovation.
- Mentor and guide junior team members, fostering a culture of knowledge sharing and continuous learning.
What you'll bring:
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
- 3+ years of industry experience working as a Machine Learning Engineer, ideally in the retail sector.
- Strong programming skills in Python/Java.
- Demonstrated expertise in developing and deploying machine learning models at scale.
- Demonstrated expertise in developing and deploying machine learning models at scale.
- Solid understanding of deep learning architectures, natural language processing, and computer vision techniques.
- Experience with prompt engineering
- Experience with deep learning/LLM-based products or platforms.
- Proficiency in data manipulation, feature engineering, and model evaluation.
- Experience working with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark).
- Experience with design and architecture, and with testing/launching software products.
- Excellent problem-solving and analytical thinking abilities, with a strong attention to detail.
- Strong communication and collaboration skills, with the ability to effectively convey complex ideas to both technical and non-technical stakeholders.
- Proven track record of delivering high-quality machine learning solutions in a fast-paced, agile environment.