Senior Data Analyst, Fraud and Identity

Instacart

2d•$133,000 - $140,500•Remote

About The Position

We are seeking a highly motivated, analytical, and organized individual to join the team who will leverage a combination of deep analytics skills and business acumen to identify, extract, and visualize opportunities across all areas of Fraud, which can then be used to drive meaningful improvements across various segments of the company. This role requires excellent time-management, effective communication skills for engaging with stakeholders at all levels, and a passion for continuous improvement, data integrity, and scalable solutions. This role focuses on utilizing data to uncover insights, develop strategies, and drive improvements across Payments, billing, and fraud processes—spanning everything from the user checkout experience to internal financial reporting. In this role, you’ll play a critical part in transforming how teams across North America leverage data to deliver exceptional performance and create industry-leading customer, shopper, and retailer experiences.

Requirements

Minimum 6 - 8 years of analytical experience, preferably in payments, fraud or identity domains
Ability to Identify potential root causes contributing to potential change(s) in metrics and provide recommendation on mitigation strategy
Strong analytical mindset with a track record of resolving complex transaction issues.
High proficiency in SQL, with experience writing complex queries, joins, and optimizations.
Experience with R/Python (fluency in at least one language preferred)
Experience with analytical visualization tools such as Mode, Tableau, Looker, Periscope or similar tools
Understanding of A/B testing and other forms of statistical analysis using statistical packages
Extremely strong verbal and written communication skills, including the ability to synthesize complex topics and create compelling narratives for various audiences
Ability to work effectively with internal stakeholders, including data scientists and data engineers. Work cross functionally with Product, Engineering, Operations to drive changes
Excellent teamwork skills and desire to help others learn
High level of accountability and ownership – driven and focused self-starter
Strategic mindset – the ability to think ahead of where the company is at now
Experience in e-commerce, CNP payments, finance, SaaS, digital goods or marketplace industry.
Experience in driving understanding of customers and their interaction with products, through in-depth analyses of user behavior and engagement

Nice To Haves

Experience in Fraud, Reconciliation, Identity or Transaction Risk field
Experience working with authentication, anti-fraud systems and checkout, tools or vendors
Advanced degree in statistics or other quantitative fields
Experience with experimentation, Data modeling, ETL and data pipeline development experience
Proficiency with AI tools (e.g., Claude, ChatGPT, Copilot) and a demonstrated ability to integrate them into day-to-day workflows.

Responsibilities

Monitor and improve critical KPIs such as fraud costs, chargeback volume, fraud rates and revenue leakage.
Regularly communicate outcomes and insights to cross-functional stakeholders, including senior leadership, to guide strategic decision-making for process and performance improvement.
Work alongside DS & Broader Fraud & Identity analysts to implement robust models in DBT and Snowflake, and design dashboards that provide a unified view of business-critical Shopper fulfillment, Shopper Payment and PoS (point of sale) Checkout data.
Work closely with Compliance and Legal teams to meet reporting requirements and successfully navigate audits.
Work with Product, Data Science, and Xfn Analytics teams to understand growth and risk factors related to new and future fraud vectors.
Analyze shopper behavioral signals to identify anomalous patterns indicative of fraud or policy abuse.
Design and conduct A/B and multivariate tests by developing test plans, defining success metrics, analyzing, and transforming data into actionable insights.
Collaborate with the Engineering and Product team to instrument new product features and ensure great event tracking and data integrity.
Build automated reporting systems to keep operations leaders informed of trends, variations, and opportunities across geographies and teams.