WHO YOU’LL WORK WITH
You will partner with globally distributed engineering, product, and program teams to ensure that the ecosystem of services remains loosely coupled, independently scalable, and aligned with business needs. You will work with your peers to develop key innovation features and report to the Engineering Manager for Marketing Technology.
WHO WE ARE LOOKING FOR
We are seeking a Senior Data Scientist who will drive advanced analytics, predictive modeling, and machine learning solutions across Marketing Technology’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.
Skillset Required
Bachelor’s degree in related field. Will accept any suitable combination of education, experience and training.
5+ years of experience in data science, machine learning, applied statistics, or advanced analytics.
Strong experience with customer, marketing, and digital analytics domains, including segmentation, personalization, propensity modeling, churn prediction, customer lifetime value (CLV/LTV), attribution, media measurement, and audience targeting.
Strong proficiency in Python, SQL, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
Experience with Generative AI, LLMs, RAG architectures, AI agents, prompt engineering, and model evaluation frameworks, including responsible AI considerations.
Experience deploying ML models into production environments and working with cloud platforms (AWS, Azure, or GCP), Databricks ML, MLflow, Spark, Delta Lake, Snowpark, and Vector Databases.
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
Build and evolve membership intelligence, audience targeting, personalization, and consumer decisioning capabilities across marketing channels.
Develop AI-powered marketing intelligence solutions, including audience recommendations, content optimization, and campaign effectiveness measurement.
Lead the design of LLM and Generative AI use cases within Martech, ensuring scalable, secure, and measurable business outcomes.
Drive adoption of AI-native data science practices and establish best practices for model governance, evaluation, and operational excellence.
Build a better practice around our marketing targeting intelligence and able to manage of targeting / data science models used from membership (lifecycle/churn/LTV/affinities) for advertising and comms.
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.
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.
