Data Scientist

analytics capabilities. This role is a senior individual contributor position focused on

identifying high-value modeling opportunities, shaping the predictive modeling roadmap,

and translating business questions into scalable analytical solutions.

This is not a people management role, but it is a leadership role. You will act as a

functional leader for predictive modeling work across the organization - helping define

priorities, guiding technical direction, and partnering very closely with our Advanced

Analytics team in to deliver high-quality modeling solutions.

You'll sit at the intersection of business context and technical execution. That means

working closely with Strategy Analytics and business stakeholders to frame the right

problems, while partnering with technical peers to ensure models are rigorous,

production-minded, and useful in practice. This is a high-impact role for someone who

can move comfortably from ambiguous business questions to deployable predictive

solutions.

Lead Predictive Modeling Roadmap & Prioritization

• Identify, size, and prioritize predictive modeling opportunities across customer,

marketplace, marketing, and operational use cases.

• Help shape the roadmap for modeling work based on business value, feasibility,

data readiness, and organizational priorities.

• Partner with Strategy Analytics, Product, Marketing, Merchandising, Finance, and

other stakeholders to translate ambiguous business questions into clear data

science opportunities.

• Ensure predictive work is aligned to measurable business outcomes, not just

technical exploration.

Build High-Impact Predictive Solutions

• Design, build, validate, and refine predictive models that improve decision-

making across the business.

• Apply the right methods for the problem, including classification, regression,

forecasting, segmentation, propensity modeling, and related machine learning or

statistical techniques.

• Develop approaches for use cases such as customer re-engagement, retention,

demand forecasting, scoring, prioritization, and other predictive decision-support

workflows.

• Define success metrics, validation frameworks, and measurement approaches

that balance model performance with business usefulness.

• Partner on deployment and activation so model outputs can be embedded into

reporting, workflows, or downstream decision processes.

Partner Closely with Advanced Analytics Team

• Work hand-in-hand with the Advanced Analytics team to scope

projects, define requirements, review approaches, and maintain quality.

• Functionally lead modeling work across a distributed team structure, even

without direct people management responsibility.

• Create clarity around priorities, deliverables, timelines, and modeling standards

so work moves efficiently and consistently.

• Support a strong operating rhythm across roadmap planning, project execution,

review, and continuous improvement.

Improve Modeling Standards & Ways of Working

• Help establish best practices for feature design, validation, model documentation,

interpretability, monitoring, and refresh cadence.

• Contribute to reusable approaches for model development, experimentation,

scoring, and model performance management.

• Partner with analytics engineering and data platform peers to improve ML-ready

datasets, feature pipelines, and activation-ready data products.

• Raise the bar on how predictive analytics is communicated so stakeholders

understand both the opportunity and the limitations of the work.

Influence Decisions Across the Organization

• Present modeling results and recommendations clearly to technical and non-

technical audiences.

• Explain what the model says, why it matters, how confident we should be, and

what the business should do next.

• Act as a thought partner to leaders by connecting predictive outputs to

prioritization, planning, and execution decisions.

• Help the organization mature from descriptive analytics toward more forward-

looking, model-informed decision-making.

Technical Environment & Tools

This role will work in a modern analytics environment centered on tools and practices

already reflected across our analytics hiring, including Snowflake, dbt, Sigma,

orchestration frameworks, and AI-assisted development workflows., We're looking for a Senior Data Scientist to help accelerate our predictive

analytics capabilities. This role is a senior individual contributor position focused on

identifying high-value modeling opportunities, shaping the predictive modeling roadmap,

and translating business questions into scalable analytical solutions.

This is not a people management role, but it is a leadership role. You will act as a

functional leader for predictive modeling work across the organization - helping define

priorities, guiding technical direction, and partnering very closely with our Advanced

• SQL for data extraction, transformation, feature development, and analytical QA

• Python for modeling, experimentation, analysis, and automation

• Machine learning and statistical libraries such as scikit-learn, statsmodels,

XGBoost, or similar tools

• Snowflake or a comparable cloud data warehouse

• Familiarity with dbt and modern analytics engineering practices

• Experience working with BI and decision-support tools such as Sigma, Tableau,

Looker, or similar platforms

• Comfort working with orchestration frameworks such as Airflow, Dagster, or

Prefect

• Experience with version control and collaborative development practices such as

Git

• Familiarity with model monitoring, feature pipelines, scoring workflows, and

production-minded analytics processes

• Comfort using AI-assisted development tools responsibly for coding,

documentation, workflow acceleration, and analysis

Required

• 5-8+ years of experience in data science, predictive analytics, machine learning,

or a related quantitative field.

• Strong hands-on experience building predictive models in business settings, not

just academic or experimental environments.

• Strong foundation in statistics, machine learning, model evaluation, and

experimental thinking.

• Strong SQL and Python skills.

• Experience taking work from problem framing through model development,

validation, and business adoption.

• Ability to translate ambiguous business problems into structured analytical

approaches.

• Experience influencing priorities and leading complex workstreams without direct

people management authority.

• Strong communication skills and the ability to explain technical work clearly to

non-technical stakeholders.

• Experience partnering effectively with cross-functional stakeholders and

distributed technical teams.

• A practical mindset: you know when to optimize for rigor, when to optimize for

speed, and how to deliver work that is actually useful to the business.

Preferred

• Experience in e-commerce, marketplace, customer, pricing, lifecycle, or growth

analytics.

• Experience with forecasting, customer propensity modeling, retention modeling,

or re-engagement use cases.

• Experience partnering with analytics engineering or data platform teams on

feature pipelines, governed datasets, and activation workflows.

• Experience productionizing model outputs into dashboards, operational

workflows, or downstream systems.

• Familiarity with model monitoring, retraining approaches, and ML ops concepts.

• Advanced degree in statistics, economics, mathematics, computer science, data

science, or a related quantitative field.