We are looking for a Data Scientist – ML & Generative AI with strong hands-on experience across traditional Machine Learning, Deep Learning, Generative AI, and Computer Vision. The ideal candidate will be able to translate business problems into scalable AI/ML solutions and take models from experimentation through production deployment.
The role will have a particular focus on building AI solutions for Supply Chain, Operations, Forecasting, Optimization, and Vision-based use cases. The candidate should be comfortable working with structured, unstructured, image, and text data and collaborating with business, engineering, product, and data teams.
Key Responsibilities
Machine Learning & Predictive Analytics
- Design, develop, and evaluate machine learning models for classification, regression, forecasting, clustering, recommendation, anomaly detection, and optimization problems.
- Apply statistical and machine learning techniques such as Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost/LightGBM, clustering, and time-series modeling.
- Perform feature engineering, feature selection, model tuning, validation, and performance analysis.
- Build reusable and scalable ML pipelines for real-world business applications.
Deep Learning
- Develop deep learning solutions using frameworks such as PyTorch and/or TensorFlow.
- Work with neural network architectures including CNNs, RNN/LSTMs, Transformers, and other modern deep learning architectures.
- Evaluate and optimize deep learning models for accuracy, latency, and scalability.
Generative AI & LLMs
- Design and develop GenAI applications leveraging Large Language Models (LLMs).
- Build solutions using techniques such as:
- Prompt engineering
- Retrieval-Augmented Generation (RAG)
- Embeddings and vector search
- LLM orchestration
- Function/tool calling
- AI agents and multi-step workflows
- Fine-tuning / parameter-efficient fine-tuning where appropriate
- Work with structured and unstructured enterprise data to create domain-specific GenAI applications.
- Develop evaluation frameworks for LLM applications covering response quality, hallucination, groundedness, relevance, latency, and cost.
- Implement appropriate guardrails and responsible AI practices for production GenAI solutions.
Computer Vision
- Develop and deploy computer vision solutions for use cases such as:
- Image classification
- Object detection
- Image segmentation
- OCR and document/image understanding
- Visual inspection and defect detection
- Product/image recognition
- Work with modern computer vision architectures and pretrained/foundation models.
- Experience with OpenCV, YOLO, CNNs, Vision Transformers, or multimodal models is desirable.
Supply Chain & Operations Analytics
Develop AI/ML solutions addressing supply-chain and operational problems such as:
- Demand forecasting
- Sales forecasting
- Inventory optimization
- Stock-out / overstock prediction
- Replenishment recommendations
- Lead-time prediction
- Supply and demand imbalance detection
- Logistics and transportation analytics
- ETA prediction
- Warehouse analytics
- Supplier performance and risk analytics
- Product allocation and assortment optimization
- Anomaly detection
- Scenario planning and decision-support solutions
The candidate should be able to work closely with supply-chain stakeholders to convert business requirements into analytical and AI/ML solutions.
Model Deployment & MLOps
- Collaborate with engineering teams to deploy ML and GenAI solutions into production.
- Develop APIs, batch pipelines, or real-time inference services for model consumption.
- Apply good software engineering practices including modular development, version control, testing, documentation, and code reviews.
- Understand concepts such as model monitoring, drift detection, model versioning, experimentation, and retraining.
- Experience with Docker, CI/CD, MLflow, Kubernetes, or similar MLOps technologies is desirable.
Required Technical Skills
Programming
- Strong Python programming skills
- SQL and data manipulation
- Pandas, NumPy, Scikit-learn
Machine Learning
- Scikit-learn
- XGBoost / LightGBM or equivalent
- Statistical modeling
- Time-series forecasting
- Feature engineering and model evaluation
Deep Learning
- PyTorch and/or TensorFlow
- Transformers
- CNNs and modern neural-network architectures
Generative AI
- LLMs and foundation models
- RAG
- Prompt engineering
- Embeddings
- Vector databases
- LLM evaluation
- Agentic AI concepts
- Frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent
Computer Vision
- OpenCV
- Object detection / segmentation models
- CNNs / Vision Transformers
- OCR and image-processing techniques
Data & Cloud
- Experience working with large datasets and cloud-based data platforms.
- Exposure to AWS, Azure, or GCP.
- Familiarity with Databricks, Spark, Snowflake, or similar platforms is an advantage.
Qualifications & Experience
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
- Approximately 4–7 years of relevant industry experience in Data Science, Machine Learning, or AI.
- Strong hands-on experience developing end-to-end ML solutions.
- Practical experience with at least one of Generative AI, Computer Vision, or advanced Deep Learning, with willingness and ability to work across all areas.
- Experience solving supply-chain, retail, manufacturing, logistics, or operations-related problems is strongly preferred.
- Experience taking ML models beyond proof-of-concept into production or business adoption.
What We Are Looking For
The successful candidate will demonstrate:
- Strong problem-solving and analytical thinking.
- Solid understanding of ML fundamentals rather than reliance solely on pre-built GenAI APIs.
- Ability to select the right approach across statistical methods, traditional ML, deep learning, computer vision, and GenAI based on the business problem.
- Ability to communicate complex analytical concepts to both technical and non-technical stakeholders.
- Strong experimentation mindset with an ability to measure business and model impact.
- Curiosity to continuously learn and apply emerging AI technologies.
- Ability to independently own moderately complex projects while collaborating effectively with senior data scientists, engineers, product teams, and business stakeholders.
Preferred / Good-to-Have Experience
- Experience with retail, consumer products, manufacturing, or supply-chain domains.
- Experience building production-grade RAG or enterprise GenAI applications.
- Exposure to multimodal AI combining text, image, and structured data.
- Experience with optimization / operations research techniques.
- Experience with forecasting at scale across products, locations, or customers.
- Knowledge of responsible AI, explainability, privacy, and AI governance.
- Experience working in Agile product or AI development teams.
Don’t meet every single requirement? At Mattel, we are dedicated to an inclusive workplace and a culture of belonging. If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we still encourage you to apply. You may be just the right candidate for this or other roles.
How We Work:
We are a purpose driven company aiming to empower generations to explore the wonder of childhood and reach their full potential. We live up to our purpose employing the following behaviors:
- We collaborate: Being a part of Mattel means being part of one team with shared values and common goals. Every person counts and working closely together always brings better results. Partnership is our process and our collective capabilities is our superpower.
- We innovate: At Mattel we always aim to find new and better ways to create innovative products and experiences. No matter where you work in the organization, you can always make a difference and have real impact. We welcome new ideas and value new initiatives that challenge conventional thinking.
- We execute: We are a performance-driven company. We strive for excellence and are focused on pursuing best-in-class outcomes. We believe in accountability and ownership and know that our people are at their best when they are empowered to create and deliver results.
Our Approach to Flexible Work:
We embrace a flexible work model designed to empower a culture of growth, optimism, and wellbeing, where every employee can reach their full potential. Combining purposeful in-person collaboration with flexibility, our focus is to optimize performance and drive connection for moments that matter.
Who We Are:
Mattel is a leading global toy and family entertainment company and owner of one of the most iconic brand portfolios in the world. We engage consumers and fans through our franchise brands, including Barbie, Hot Wheels, Fisher-Price, American Girl, Thomas & Friends, UNO, Masters of the Universe, Matchbox, Monster High, MEGA and Polly Pocket, as well as other popular properties that we own or license in partnership with global entertainment companies. Our offerings include toys, content, consumer products, digital and live experiences. Our products are sold in collaboration with the world’s leading retail and ecommerce companies. Since its founding in 1945, Mattel is proud to be a trusted partner in empowering generations to explore the wonder of childhood and reach their full potential.
Mattel’s award-winning workplace culture has been recognized by Forbes, Fast Company, Newsweek, Great Place to Work, TIME, and more.
Visit us at and .
Mattel is an Equal Opportunity Employer where we want you to bring your authentic self to work every day. We welcome all job seekers, and all applicants will receive consideration for employment.
Videos to watch:
The Culture at Mattel
Corporate Philanthropy