Our hiring partner, a globally recognized portfolio of lifestyle brands, is seeking a visionary Senior Data Scientist to architect and deploy next-generation visual experiences driven by Generative AI. This role is a pivotal technical leadership position focused exclusively on the advancement of image and video generation to revolutionize how the company creates, scales, and personalizes digital content.

This individual will lead the charge in developing proprietary algorithms and orchestrating complex Generative AI pipelines that fuel innovation across our entire digital ecosystem. Working at the intersection of cutting-edge machine learning and visual aesthetics, you will be responsible for creating production-ready solutions that span from automated creative asset generation to immersive virtual experiences.

As a Senior Data Scientist, you will be a key player in building the foundational AI infrastructure that drives the brand's digital presence. You will drive the end-to-end lifecycle of generative models, from research and prototyping to implementation and validation, ensuring that AI-generated content not only meets technical benchmarks but also adheres to the company's high standards for visual storytelling and brand integrity.

This role demands a unique hybrid: deep technical fluency in generative architectures paired with a sharp visual sensibility. The ideal candidate possesses a solid foundation in traditional machine learning but is specifically grounded in the rapid advancements of diffusion models, multimodal systems, and modern orchestration frameworks. You are not just an engineer; you are a strategic thinker who can evaluate outputs with a critical creative eye, ensuring AI enhances rather than detracts from the human-centric design ethos of the company.

If you are passionate about the convergence of generative AI and visual storytelling, constantly experiment with emerging technologies, and are eager to shape the future of AI-native commerce, we invite you to build something extraordinary with us.

Role Responsibilities

1. Pipeline Architecture: Design, implement, and optimize high-fidelity image and video generation pipelines using state-of-the-art generative models to produce visual content at a commercial scale.
2. Workflow Orchestration: Build and maintain robust, multi-model generative workflows utilizing orchestration tools to chain complex processes—including generation, inpainting, upscaling, style transfer, and conditioning—into seamless, production-ready systems.
3. Model Adaptation: Fine-tune and adapt foundational image generation models through advanced techniques (e.g., LoRA, DreamBooth, ControlNet) to ensure strict brand consistency, style adherence, and controlled output.
4. Multimodal Integration: Leverage vision-language models (VLMs) and multimodal data sources for image understanding, automated tagging, visual analysis, and quality evaluation within generative pipelines.
5. Video Innovation: Evaluate, prototype, and integrate emerging video generation models into creative workflows, pushing the boundaries of dynamic content creation.
6. Agentic Systems: Develop agentic AI pipelines capable of orchestrating multi-step visual content creation, automating workflows from initial prompt engineering to final image synthesis and delivery.
7. Cross-Functional Collaboration: Partner closely with Creative, Product Management, and Engineering teams to translate brand objectives and business challenges into scalable, high-impact generative AI solutions.
8. Technical Leadership: Lead the technical evaluation of new generative AI models, tools, and vendors, guiding the strategic evolution of the company’s visual AI technology stack.
9. Data Strategy: Guide data curation and preparation strategies for fine-tuning, including dataset construction, annotation workflows, and the strategic use of synthetic data to improve model performance.
10. Quality Assurance: Analyze and benchmark model outputs for quality, consistency, and alignment with brand identity. Design robust validation loops that integrate quantitative metrics with qualitative human assessment.
11. Production Engineering: Partner with software engineers to translate research prototypes into production-grade services and APIs, with a focus on cost optimization, latency reduction, and high throughput.

Role Qualifications

Must-Have

1. Experience: 5+ years of industry experience in data science, machine learning, or AI engineering, with a strong foundation in ML fundamentals. Minimum of 1+ year of hands-on experience specifically working with image generation models in a professional or applied research setting (beyond casual personal projects).
2. Technical Proficiency: Strong command of Python and deep learning frameworks, specifically PyTorch, with practical experience using the Hugging Face ecosystem for multimodal models.
3. Advanced Techniques: Demonstrated experience with fine-tuning and conditioning techniques for image models (e.g., LoRA, DreamBooth, ControlNet, IP-Adapter, Textual Inversion).
4. Orchestration: Working knowledge of GenAI workflow orchestration tools for building multi-step generation pipelines.
5. Multimodal Expertise: Experience using vision-language models for image understanding, captioning, or visual analysis.
6. Infrastructure: Experience with cloud-based AI infrastructure (e.g., AWS, GCP, Azure) for training, fine-tuning, and serving generative models at scale.
7. Adaptability: Proven ability to rapidly evaluate and adopt new generative AI models and tools as the field evolves.
8. Aesthetic Sensibility: Strong visual sensibility, with a discerning eye for image quality, composition, and brand consistency in generated outputs.
9. Soft Skills: Excellent communication and collaboration skills, with a demonstrated ability to bridge the gap between technical and creative teams.
10. Education: Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Engineering, or Mathematics, or equivalent practical experience.

Nice-to-Have

1. Experience with video generation models and a solid understanding of the evolving video GenAI landscape.
2. Hands-on experience with creative design tools such as Adobe Photoshop, Firefly, or Figma, particularly with AI-augmented features like generative fill and inpainting.
3. Experience building agentic AI workflows to orchestrate complex, multi-model pipelines.
4. Familiarity with fashion, retail, or e-commerce applications of generative AI (e.g., virtual try-on, AI product photography, on-model generation).
5. Background in core computer vision fundamentals (e.g., segmentation, detection, embeddings) that complements generative work.
6. Experience with systematic prompt engineering at scale, including developing prompt libraries and structured strategies for consistent visual output.