Lead Data Scientist

Mattel•East Aurora, NY

3h•$104,000 - $115,000•Onsite

About The Position

The Product Quality Analytics team operates within the Global Quality, Safety, Sustainability and Regulatory Compliance organization and provides data, analytics, insights, and data-driven decision support across Mattel brands. The team is responsible for delivering trusted reporting, interactive dashboards, and advanced analytics that help the organization understand product performance, consumer feedback, safety trends, and risk indicators. These insights support product development, corrective action effectiveness, and continuous improvement initiatives, ensuring Mattel delivers the highest-quality products to consumers worldwide. We are seeking a Lead Data Scientist to drive advanced analytics and AI initiatives within our Product Quality Analytics organization. This role sits at the intersection of data science, product quality, and business strategy, and is responsible for leading high impact initiatives that transform complex data into scalable insights and intelligent systems. You will own the end-to-end lifecycle of data science solutions, from problem framing and modeling to deployment and adoption, while influencing stakeholders and mentoring team members. This is a highly visible role with the opportunity to shape how analytics and AI are embedded into quality decision making across the enterprise.

Requirements

Degree in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field
5 or more years of experience in data science with demonstrated ownership of end to end projects
Experience leading projects or mentoring team members in a formal or informal capacity
Strong programming skills in Python or R
Advanced SQL and experience working with large scale datasets
Experience with machine learning frameworks
Familiarity with cloud platforms, preferably Google Cloud (BigQuery, Vertex AI)
Experience working with unstructured data such as documents, videos, or images is a strong plus
Deep knowledge of statistical modeling techniques including regression, experimentation, and forecasting
Experience with A B testing, causal inference, and predictive modeling
Strong problem solving skills with the ability to connect data insights to business strategy
Experience with prompt and context engineering
Experience with agentic workflows and platforms
Proven ability to influence stakeholders and drive decisions through data
Strong communication skills with the ability to simplify complex topics
Comfortable working in ambiguous and fast paced environments
Demonstrated a growth mindset by staying curious and continuously learning, embracing challenges, and improving themselves.

Responsibilities

Lead the design and execution of advanced analytics and machine learning initiatives aligned to product quality and consumer insights
Partner with key stakeholders to define high value business problems and translate them into scalable data science solutions
Act as a thought leader in AI and machine learning, driving adoption of modern techniques including generative AI, natural language processing, and predictive modeling
Develop and deploy models such as classification, forecasting, anomaly detection, natural language processing, and causal inference
Apply statistical techniques such as regression, time series analysis, and experimentation to solve complex business challenges
Build frameworks for early signal detection, quality risk prediction, and root cause analysis
Collaborate with data engineering to productionize models using Google Cloud Platform tooling such as Agent Platform, Cloud Run, and Dataform
Contribute to scalable data products and pipelines that enable self service analytics
Ensure models are monitored, maintained, and continuously improved with an MLOps mindset
Partner with data engineering, analytics, and business teams to align on priorities, data needs, and solution design
Work across teams to ensure data consistency, shared understanding of metrics, and scalable solutions
Bridge technical and non technical stakeholders by communicating clearly and driving adoption of data science solutions