WHO YOU’LL WORK WITH
This role typically reports to a Senior Manager, Software Engineering within Global Converse ITC.
Global Converse ITC Data Science, Analytics, Engineering, and Architecture teams
Nike Global Technology (Enterprise Data & Analytics, ML Platform teams)
Product Management, Marketplace, Planning, Demand & Supply, Finance, Consumer Insights, and Digital teams
External analytics partners and platform vendors
WHO WE ARE LOOKING FOR
We are seeking a Lead Data Scientist who will drive advanced analytics, predictive modeling, and machine learning solutions across Converse’s global digital and enterprise ecosystem. You bring deep technical expertise combined with business acumen, enabling you to translate complex data into actionable insights and high-impact models. You thrive in ambiguous environments, collaborate naturally with cross-functional partners, and elevate data maturity through innovation, quality, and thoughtful leadership.
Details on qualifications:
Bachelor’s degree in related field. Will accept any suitable combination of education, experience and training.
6+ years of experience in data science, machine learning, applied statistics, or advanced analytics.
Strong proficiency in Python, SQL, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
Experience deploying ML models into production environments and working with cloud platforms (AWS, Azure, or GCP).
Expertise in predictive modeling, optimization, experimentation, and advanced statistical methods.
Strong understanding of data engineering concepts, feature stores, and ML Ops principles.
Familiar with Databricks, Snowflake and other database foundational tools.
Experience with visualization tools and storytelling for technical and non-technical audiences.
Ability to work cross-functionally, influence stakeholders, and translate complex analysis into actionable strategies.
Strong communication skills, curiosity, ownership mindset, and commitment to quality.
WHAT YOU’LL WORK ON
Develop, deploy, and maintain machine learning models that accelerate decision-making across product, marketplace, consumer, and operational teams.
Partner with engineering, analytics, and product teams to build scalable data science pipelines and integrate models into production environments.
Conduct exploratory analysis, identify meaningful patterns, and translate findings into clear narratives and recommendations.
Build predictive and optimization models for demand forecasting, pricing, allocation, personalization, supply chain optimization, and business performance.
Ensure model quality through rigorous testing, validation, drift monitoring, and performance measurement.
Drive experimentation frameworks, A/B testing design, and causal inference models to inform product and business strategy.
Collaborate with Nike Technology partners to align with enterprise data, ML, and platform standards.
Mentor junior and mid-level data scientists and analysts, fostering excellence in modeling, coding, and problem-solving.
Evaluate new tools, frameworks, and techniques; lead proofs of concept and guide strategic adoption of data science capabilities.
