Data Scientist

About the Role

Join Zenni Optical's AI & Machine Learning team as a Senior Staff Data Scientist, where you'll lead transformative initiatives that impact millions of eyewear customers worldwide. In this pivotal role, you'll drive innovation across computer vision, personalized experiences, and AI-powered solutions that directly influence revenue growth, customer retention, and operational efficiency.

As a technical leader, you'll combine deep machine learning expertise with mentorship responsibilities, spending most of your time on hands-on technical work while guiding junior data scientists. You'll own the complete lifecycle of high-impact ML projects from initial problem framing through production deployment and performance optimization.

This onsite position offers the unique opportunity to shape AI strategy at a leading eyewear company, working with cutting-edge ML infrastructure to solve complex challenges in computer vision, recommendation systems, and personalization at scale.

Responsibilities:

• Own end-to-end ML project lifecycle from problem framing and data preparation through model deployment, monitoring, and performance optimization.
• Develop advanced document understanding solutions using OCR and LLMs for automated processing workflows.
• Design and implement personalized recommendation systems that drive product discovery and enhance customer experience.
• Build sophisticated NLP and LLM-powered solutions for conversational AI and automated response generation.
• Create predictive models for customer behavior analysis, demand forecasting, and personalization using advanced machine learning and statistical techniques.
• Conduct rigorous experimentation and A/B testing to validate model performance and quantify business impact.
• Monitor and optimize model performance in production environments, proactively identifying drift, bias, and improvement opportunities.
• Establish robust ML system architecture and scalable deployment patterns for high-availability production systems.
• Lead code reviews and enforce technical standards across the data science team.
• Resolve complex modeling and statistical challenges, providing expert guidance on advanced techniques and methodologies.
• Build data pipelines and analytics frameworks for both batch and real-time processing at scale.
• Mentor and coach junior data scientists, providing technical guidance, career development support, and project leadership.
• Partner with cross-functional stakeholders to translate business requirements into technical solutions and effectively communicate results to leadership.

Basic Qualifications: 

• BS or MS in Computer Science, Applied Mathematics, or a related field.
• 10+ years of data science experience with demonstrated progression to senior technical roles and proven track record of leading high-impact ML projects.
• Solid Python programming skills and a solid software engineering foundation.
• Expert-level proficiency in SQL with experience in data manipulation and analysis at scale.
• Deep expertise in machine learning and statistics, including supervised/unsupervised learning, deep learning frameworks, and statistical modeling techniques.
• Computer vision and NLP experience, particularly with document understanding.
• Proven experience with recommendation systems design, implementation, and optimization.
• Strong project and people leadership skills, with experience mentoring team members and driving cross-functional initiatives.
• Excellent written and verbal communication skills, with ability to articulate complex technical concepts to diverse audiences.
• Open-minded approach to problem-solving and curiosity to explore innovative AI solutions.

Preferred Qualifications:

• Familiarity with cloud computing platforms, such as GCP, DataBricks.
• Demonstrable project experience in deep learning applications with real-world impact.
• Hands-on experience with production LLM-based applications, including deployment, fine-tuning, and optimization.
• E-commerce or retail industry experience with understanding of recommendation and personalization challenges.
• Deep learning specialization in computer vision or natural language processing.
• Experience with real-time ML systems handling millions of users with low-latency requirements.
• Familiarity with ML lifecycle management tools and ML platform development.