Data Scientist

BuyWander•Kent, WA

1d•$90,000 - $110,000•Onsite

About The Position

The Data Scientist in Buywander’s Data & AI department builds and operates the models, experiments, and data foundation used to guide company decisions. The role combines applied machine learning, experimentation, ownership of data quality and definitions, and analysis that turns data into business recommendations. Its mission is to establish trusted data, production models that improve over time, and faster, better-measured decisions. The Data Scientist monitors, reports on, retrains, and improves production decision models; designs and evaluates experiments; maintains reliable data pipelines and documented metrics; and conducts analyses for leaders, operators, and General Managers. The role maintains the GM scorecard and operating dashboards in MetaBase, communicates findings to audiences ranging from store managers to the board, and partners with Engineering on data model and infrastructure changes. The position is based on-site in Kent, Washington, with periodic time in stores and occasional travel to company markets and training seminars. Success means leaders trust the data and models, core metrics are traceable and documented, stakeholders rely on the Data Scientist for data questions, and model improvements or experiments measurably improve business outcomes. BuyWander describes its team culture through the 4H’s: Happy, Humble, High Performing, and (H)Loyal.

Requirements

Master’s degree in data science, statistics, analytics, computer science, or a related field.
2 to 4 years of experience in data science, applied analytics, or a similar role.
Applied machine learning experience, including building and validating models, with exposure to deployment and monitoring.
Knowledge of statistics and experiment design, including A/B testing and forecasting.
Advanced SQL and Python skills, including pandas, scikit-learn or similar tools, notebooks, and scripts for recurring work.
Experience working with a data model or pipeline.
Experience building dashboards in a BI tool such as Metabase, Tableau, or Looker.
Strong attention to detail and ability to reconcile conflicting numbers and document metric definitions.
Ability to handle confidential company and financial data with discretion.
Strong organizational and time-management skills and ability to manage multiple priorities.
Excellent written and verbal communication skills, including explaining findings to non-technical audiences.
Hands-on experience using LLMs such as Claude in personal work, including analysis, coding, or automation.
Must reside and work in the United States and be legally authorized to work in the U.S. without current or future employer sponsorship.

Nice To Haves

Experience in e-commerce, marketplace, retail, or operations businesses.
Causal inference experience.
Experience with dbt or a similar data transformation tool.
Cloud data platform experience, such as AWS, Snowflake, or Databricks.
Git experience and code review habits.
Experience with auction or marketplace data.

Responsibilities

Monitor production decision models, report on their performance, and retrain and improve them over time.
Design, run, and evaluate experiments within the A/B testing and feature-flag program, including defining experiment metrics.
Assess what the data can and cannot support and recommend appropriate actions.
Maintain clean, reliable, and documented data pipelines, and incorporate new data sources as needed.
Maintain and document KPI and metric names, definitions, calculation logic, sources, owners, refresh frequencies, and intended uses.
Understand the contents and meanings of tables and columns and identify conflicting definitions.
Validate data when business logic or product behavior changes and flag discrepancies before they reach dashboards.
Partner with Engineering on changes to the data model and infrastructure.
Conduct ad hoc analyses for leadership, operators, and General Managers, and provide clear recommendations.
Frame analyses around what happened, why it happened, whether results are good or bad versus plan, and what action should follow.
Maintain reporting relied on by the company, including the GM scorecard and operating dashboards in MetaBase.
Communicate findings clearly in writing and in person to audiences ranging from floor managers to the board.
Coordinate with partner teams such as Finance, Operations, and Tech.
Perform additional responsibilities and strategic initiatives as assigned.