Summary

Since Block's inception, our innovative and technology-forward approach to risk management and customer protection has been fundamental to how we invent and build financial products. The Risk team at Block continues this legacy through a sophisticated, technology and science-led approach to protecting our customers and their funds. Our interdisciplinary structure combines Product Development, Science teams (specializing in modeling, analytics, and data science), Operations and key partners including Legal Counsel and Policy, all working in concert to identify, assess, and solve complex risk challenges across access, fraud prevention and compliance.

This role is part of our Risk organization and will operate within the Compliance Data Science & Analytics team - chartered to put data at the center of the organization’s decisions. We need a data scientist with the ability to enact organizational change through their analysis of emerging risks on the platform. You will utilize a variety of methods from bad activity prevalence sampling, to customer segmentation and analysis. You’ll work with teams across compliance, risk, and product teams to identify and measure new and existing areas of unwanted behavior in order to inform controls and compliance monitoring.

You Will

1. Perform deep dive analyses into different customer segments and/or products to identify pockets of compliance risk and measure the effectiveness & impact of product controls
2. Spearhead the implementation and analysis of bad activity prevalence sampling studies. Identify and mitigate potential biases and sampling errors to enhance the accuracy and reliability of findings
3. Design and analyze experiments to assess the impact of compliance programs, leveraging A/B testing and causal inference methods to drive actionable insights 
4. Own executive-level reporting and metrics to measure risk and bad activity on the platform over time
5. Collaborate with cross-functional teams to translate insights into strategic actions and program recommendations 
6. Communicate complex data science concepts and results to both technical and non-technical audiences and influence teams to take action based on your recommendations

We’re Targeting 

1. A Level 6 hire - typical experience for L6 would be something like BSc with 7-10 years; MSc with 5-8 years; or a PhD with 3-5 years
2. A background in Statistics, Mathematics, Biostatistics, Economics or related quantitative field
3. Extensive knowledge of sampling methodologies, with a proven ability to apply them in real-world scenarios and to identify and quantify sources of bias
4. Expert-level abilities in SQL and data analysis/visualization tools (i.e. Tableau, Looker, Mode, Python libraries)
5. Proficiency in scripting and data analysis programming languages, such as Python or R
6. Deep experience using data to solve complex, ambiguous problems
7. Vision to define problems and manage projects in a fast-­paced, evolving organization
8. Track record of bringing data to life for any audience through written and verbal communication and data visualization
9. Excellent communication and leadership abilities

Technologies We Use and Teach

1. SQL, Snowflake, etc.
2. Python (Pandas, Numpy)
3. Tableau, Airflow, Looker, Mode, Prefect

Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.

To find a location’s zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.  

Zone A:

$198,000—$297,000 USD

Zone B:

$188,100—$282,100 USD

Zone C:

$178,200—$267,400 USD

Zone D:

$168,300—$252,500 USD

Skills

• Database Management
• Development
• Problem Solving
• Python
• Risk Analysis
• SQL