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Nuuly · 5 hours ago

Nuuly Senior Data Scientist

Philadelphia, PA

Full-time

Onsite

Mid, Senior Level

4+ years exp

Nuuly is seeking a Senior Data Scientist to join the analytics team and help bring data-driven insights and decision making to various functions. The role involves leading analytics initiatives, designing experiments, and serving as a liaison between technical and non-technical stakeholders to inform business strategy.

Retail

Responsibilities

Lead analytics initiatives that shape business strategy, especially in high-priority domains like inventory optimization and planning. Over time, flex across domains (e.g., marketing, logistics, finance), becoming a trusted strategic partner across the org

Bring data science capabilities, such as ML modeling and advanced statistical concepts, to the Nuuly analytics team

Design and run experiments (e.g., A/B tests, causal inference studies) to measure the impact of strategic initiatives and inform decision-making at scale

Conduct complex analyses independently in Google BigQuery (SQL) and Python. Run simulations and conduct statistical analyses on test results

Serve as a liaison between technical and non-technical stakeholders, translating data into insights

Work with stakeholders to understand the meaning & intent behind their questions, not just perform question-and-answer services. Partner with stakeholders to inform strategy, suggesting new approaches, introducing new metrics to better understand the business, etc

Qualification

PythonSQLMachine LearningStatistical AnalysisData StorytellingGoogle BigQueryML ToolsInterpersonal SkillsCommunication SkillsCritical ThinkingCreative Problem Solving

Required

4+ years in a data science or applied ML role, preferably with consumer-facing or e-commerce applications

Advanced proficiency in Python and SQL

Strong statistical and analytical skills (e.g., hypothesis testing, distributional analysis, uncertainty estimation)

Experience with a wide range of ML techniques (e.g., tree-based models, clustering, neural networks, time series forecasting) and model evaluation (troubleshooting, metrics, benchmarking)

Strong familiarity with ML tools and libraries (e.g., scikit-learn, TensorFlow, PyTorch, Jupyter Notebooks)

Ability to translate business problems into technical solutions and communicate findings clearly to technical and non-technical stakeholders