Data Scientist I

Our opportunity 

We are hiring a Data Scientist I for our Trust and Safety team. You will help us detect and reduce harmful behavior on the platform, focusing on detecting fraudulent or abusive activities. Partner with Product, Engineering, Operations, and Business stakeholders to turn data into models, dashboards, and clear recommendations that protect customers and the company. 

In this role you will move work through the full data science path: framing the problem, pulling and checking data, building features and models, measuring performance in realistic terms, and explaining what we learned and what we should do next. 

Responsibilities 

Gather, prioritize, and prepare data from multiple sources with attention to quality, definitions, and limitations to deliver scalable, data-driven solutions that improve customer trust, operational efficiency, and business outcomes. 

Run exploratory analysis with statistics and visuals to find patterns, segments, and signals relevant to risk and policy decisions. 

Engineer and select features (create, transform, validate) to improve model quality and stability over time. 

Use statistical methods (for example descriptive stats, comparisons, and hypothesis-style checks where appropriate) to describe distributions, relationships, and uncertainty. 

Choose and implement machine learning approaches suited to the problem (for example classification, ranking, clustering) given data volume and constraints. 

Evaluate models with metrics that match the business goal (for example precision/recall tradeoffs, calibration, segment performance) and iterate on the model and data pipeline. 

Interpret outputs, surface key drivers and caveats, and present results so both technical and non-technical partners can act on them. 

Build clear charts and narratives for reviews, documentation, and operational readouts. 

Partner with product, engineering, and operations to turn questions into scoped analyses and shipped solutions. 

Qualifications 

Bachelor's degree in data science, Statistics, Computer Science, or a closely related field. 

1+ years of experience in a data science or equivalent analytics role with examples of end-to-end work. 

Strong programming in Python, including data manipulation and ML libraries. 

Solid SQL and experience working with relational or warehouse-style data. 

Grounding in statistics and machine learning; comfort explaining model behavior and limitations with a specific focus on rare event detection and handling highly imbalanced datasets. 

Strong written and verbal communication; able to simplify technical work without hiding important tradeoffs. 

Nice to have 

Experience with a major cloud platform (AWS, Azure, GCP) or Snowflake. 

Familiarity with AWS data and ML services (for example Glue, SageMaker, Athena, Redshift). 

Experience with visualization or lightweight apps (for example Tableau, Streamlit) or analyst workflows that include structured querying. 

Background in retail, e-commerce, payments, fraud and risk domains.