NIKE, Inc. supporting Converse brand is looking to uncover insights about consumer preferences for our products to shape the next generation of data-driven product creation and merchandising.
Requirements
- Deep expertise in Python, with hands-on experience in building, training, scoring, tuning, and maintaining statistical and machine learning models using libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and others.
- Proven experience working with modern data platforms like Snowflake and Databricks, enabling scalable data processing, collaborative development, and efficient model deployment.
- Demonstrated success in developing and operationalizing predictive models at enterprise scale, including model lifecycle management, performance monitoring, and retraining strategies.
- Strong familiarity with mainstream tools and frameworks across the Data Science/Analytics lifecycle, such as MLflow, Airflow, and model governance platforms.
- Experience leveraging standard data science tools (PySpark, Pandas, Scikit, XGBoost, etc.), platforms (Databricks, Python, Snowflake), and cloud providers (AWS/MS Azure).
Responsibilities
- designing and developing innovative model-based solutions
- building models that uncover insights consumer preferences for products to inform Converse’s business teams and organizations
- ideate, develop, and operationalize algorithmic solutions for bringing a consumer lens to key decisions facing Product Creation, Merchandising, different planning, Operations, and consumer teams
- stay up to date on relevant industry trends and pull from your generalist data scientist toolkit to identify the right data science approach to each problem you encounter
- develop models
- extract insights from the output of your models
- scale solutions across the company
Other
- passionate about the Converse brand, energetic and proactive, and thrives on working at the intersection of data and business.
- data science generalist with passion for creating models that inform key business decisions.
- comfortable getting their hands dirty with exploratory data analysis, coding, and modeling.
- seek to ensure their solutions can be effectively leveraged by business users in a decision support capacity.
- 4-7 years of hands-on industry experience in developing data science models to inform key business decisions.
