Lead Data Scientist, Tech

Become a Part of the Converse Data Analytics Team
NIKE, Inc. supporting Converse brand is a place to explore potential, push boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream, and build. Its culture thrives by embracing diversity and exciting inventiveness. At NIKE, Inc. it's about each person bringing skills and passion to an exciting and constantly evolving game

Who We Are Looking For
Working at Converse in this time of growth and dynamic strategic intent is a unique opportunity; we are looking for a professional who is passionate about the Converse brand, energetic and proactive, and thrives on working at the intersection of data and business.

We're looking for a Lead Data Scientist, Enterprise and Data analytics, to join Converse's Enterprise

Data Analytics organization and uncover insights about consumer preferences for our products. This position will play a meaningful role in shaping the next generation of data-driven product creation and merchandising at Nike, at a time where this evolution is critical to our success.

We seek a data science generalist with passion for creating models that inform key business decisions. This person will be comfortable getting their hands dirty with exploratory data analysis, coding, and modeling. They will also seek to ensure their solutions can be effectively leveraged by business users in a decision support capacity.

A successful candidate has a comprehensive grasp of the data science development process, with breadth across a variety of techniques and depth in 1-2 techniques. They will have experience leveraging standard data science tools (PySpark, Pandas, Scikit, XGBoost, etc.), platforms (Databricks, Python, Snowflake), and cloud providers (AWS/MS Azure).

As a Lead Data Scientist, you will be responsible for designing and developing innovative model-based solutions while collaborating with team-members and business users, assuring that we are crafting the best solutions for evolving business needs.

What You Will Work On
This person will join a team that is responsible for building models that uncover insights consumer preferences for products to inform Converse's business teams and organizations.

You will work closely across different businesses to answer key questions about how to design the best products, line plans, and assortments to serve athletes and consumer experience.

You will ideate, develop, and operationalize algorithmic solutions for bringing a consumer lens to key decisions facing Product Creation, Merchandising, different planning, Operations, and consumer teams.

You will be expected to 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.

Across your projects, you will work closely with Data Science peers to develop models, with Analytics and business stakeholders to extract insights from the output of your models, and with Product Managers and Machine Learning Engineers to scale solutions across the company.

When you succeed, you will have helped establish the next generation of Product Creation and Merchandising at Nike, enabling Nike to better serve athletes* and create more innovative, successful product offerings.

Who You Will Work With
Reporting to the Director of Enterprise and Data Analytics, you will work with internal teams across business functions merchandising, GSM, Supply chain operations, Global Partners Markets, Demand Planning, Supply Planning team etc.

What You Bring

• Bachelor's degree in mathematics, Statistics, Machine Learning, Data Science, or related quantitative field preferred or equivalent combination of education, experience or training.
• 4-7 years of hands-on industry experience in developing data science models to inform key business decisions.
• Experience developing data science models for Demand Planning and/ or Consumer Research preferred.
• 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.
• Ability to define and drive the data science roadmap, align technical initiatives with business goals and ensure delivery of high-impact solutions.
• Experience in mentoring and guiding junior data scientists, fostering a culture of learning, collaboration, and technical excellence.
• Strategic thinking and leadership in prioritizing projects, managing stakeholder expectations, and translating complex data insights into actionable business recommendations.
• Excellent communication skills to influence cross-functional teams and present findings to both technical and non-technical audiences.
• A growth mindset and commitment to staying current with emerging trends in AI, machine learning, and data science best practices.

We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.