Data & Risk Strategy Enablement

This is a fully remote position, open to applicants in United States.

📋 Description

• Act as a reliable expert in data and risk for data scientists, risk strategists, engineers, and business stakeholders.

• Cultivate a thorough understanding of essential source systems, data structures, business processes, and data flows that support credit, fraud, and various risk strategies.

• Convert business and risk requirements into succinct data requirements, pinpointing suitable data sources, transformations, features, and analytical methods.

• Establish and advocate for unified definitions of risk, customer, account, transaction, and performance metrics.

• Discover opportunities to enhance data availability, usability, quality, and scalability.

• Serve as a bridge between data consumers and data engineering teams by prioritizing and articulating data requirements and technical necessities.

• Design, enhance, and refine pipelines that support risk analytics, modeling, and strategies.

• Recognize pipeline bottlenecks, redundancies, data quality problems, and potential efficiency and reliability enhancements.

• Create scalable data patterns that minimize duplicated data preparation.

• Engage in data architecture and pipeline change design and review.

• Identify upstream data modifications and source-system dependencies that influence models, strategies, reporting, or monitoring.

• Foster relationships with source-system owners and downstream data consumers.

• Act as the primary liaison between Risk Solutions and Strategy and the wider feature store ecosystem.

• Encourage feature-store adoption across data science, risk, and partner organizations.

• Establish best practices for feature development, documentation, reuse, governance, and usage.

• Oversee feature definitions, metadata, lineage, quality, ownership, and lifecycle management.

• Assist in migrating duplicated or manually maintained features into reusable, governed feature-store assets.

• Collect user feedback and convert it into enhancements and priorities for the feature-store.

• Advocate for feature reuse and minimize duplication across models and analytical solutions.

• Ensure features are easy to find, well-documented, trustworthy, and suitable for production risk applications.

• Investigate and coordinate the resolution of data issues across source systems, pipelines, and downstream consumers.

• Collaborate with data governance and technology teams to enhance lineage, ownership, documentation, and controls.

• Identify inconsistencies in data definitions and establish uniform business and technical definitions.

• Communicate data risks, dependencies, and limitations that could impact analytical or risk outcomes.

• Build credibility and relationships across data engineering, technology, risk, data science, and source-system teams.

• Influence teams without direct authority and facilitate solutions across organizational boundaries.

• Recognize reusable capabilities, common features, and scalable solutions.

⛳️ Requirements

• A minimum of 6 years of professional experience in data, analytics, data science, risk, data engineering, or related fields.

• Ideally, experience in financial services or within a complex data environment.

• Strong comprehension of data ecosystems, including source systems, data warehouses/lakes, ETL/ELT pipelines, data transformations, and downstream analytical usage.

• Proficient in SQL with experience handling large, intricate datasets.

• Ability to comprehend complex source systems and convert business processes into data requirements and analytical datasets.

• Experience collaborating with data engineering teams to construct, optimize, and troubleshoot data pipelines.

• Solid understanding of data quality principles, data lineage, metadata, data governance, and data ownership.

• Experience with feature stores, feature engineering platforms, or similar reusable data/analytics infrastructures.

• Ability to grasp data science and machine learning needs, including the development, evaluation, consumption, and operationalization of features.

• Strong knowledge of credit risk, fraud risk, payments, or other financial services risk areas.

• Excellent communication and relationship-building skills, with the capability to influence teams without direct authority.

• Ability to work independently, identify ambiguous challenges, and drive solutions across organizational boundaries.

• Strong analytical, problem-solving, and critical-thinking capabilities.

• Experience serving as a data or analytics subject matter expert across various teams or organizational functions.

• Experience in owning or assisting in the establishment of a feature store, feature platform, or enterprise analytics data product.

• Experience collaborating on shared data platforms or capabilities between data science and engineering organizations.

• Experience in developing enterprise data definitions, data standards, or data governance practices.

• Experience with cloud data platforms and modern data architectures.

• Familiarity with data cataloging, lineage, data observability, or data quality platforms.

• Experience in an environment where data science, risk strategy, and data engineering function as highly interconnected disciplines.

• Experience with credit risk modeling, portfolio management, underwriting, fraud, collections, or other financial risk applications.

• Experience influencing technical roadmaps and priorities across teams without direct management authority.

• Ability to streamline complex data environments and create reusable solutions that expedite data science and risk strategy.

• Familiarity with AI tools such as Gemini, ChatGPT, or similar.

🏝️ Benefits

• Generous paid time off.

• Paid volunteering time.

• Health insurance.

• Dental insurance.

• Vision insurance.

• Retirement savings plan.

• Health savings account.

• Flexible spending accounts.

• Life insurance.

• Disability insurance.

• Tuition reimbursement.

• Eligibility for quarterly or annual bonuses for non-sales roles.

• Comprehensive and competitive benefits package.

• Culture of diversity and inclusion.