Lead Data Scientist

Job highlights

• Bachelors degree in data science,computer science,engineering,statistics,mathematics,operations research,or a related quantitative field,or equivalent practical experience
• Experience with anomaly detection,classification,time-series analysis,risk scoring,or similar applied modeling techniques
• Experience designing and maintaining ETL or ELT pipelines using large,complex datasets

Job Summary

Role Summary Staples is seeking a Lead Data Scientist to join our Global Capability Center and support the Cyber Fraud Prevention organization. This senior individual contributor will design, develop, deploy, and maintain advanced analytics, machine learning models, and data products that help identify fraudulent activity, automate fraud decisions, monitor unusual customer and transaction behavior, and improve fraud operations.

This is an end-to-end applied data science role. The successful candidate will translate complex business problems into analytical solutions, prepare and model data, develop production-quality code, integrate internal and third-party services, deploy solutions, and monitor their ongoing performance.

The role requires a blend of data science, data engineering, and software engineering. It is best suited for someone who can independently take a solution from initial concept through production implementation and ongoing support.

Responsibilities

• Design, develop, validate, and operationalize machine learning and statistical models for e-commerce fraud, transaction risk, anomaly detection, customer behavior, and related use cases.

• Develop supervised and unsupervised learning solutions, including classification, risk scoring, anomaly detection, segmentation, and time-series modeling.

• Perform feature engineering using transaction, customer, payment, identity, device, product, order, and case-management data.

• Establish model baselines, validation methods, performance measures, and ongoing monitoring controls.

• Develop production-quality Python applications, data pipelines, APIs, and model-scoring processes.

• Write and optimize advanced SQL for analytical, operational, and model-development use cases.

• Design and maintain ETL and ELT pipelines that integrate data from enterprise systems, cloud platforms, fraud tools, and third-party providers.

• Build automated decisioning processes that combine model results, business rules, internal data, and external risk signals.

• Implement appropriate testing, logging, data-quality controls, exception handling, and operational alerting.

• Use Git-based source control and support code review, CI/CD, deployment, and production-support practices.

• Evaluate third-party fraud, identity, data, and machine-learning products through structured proofs of concept.

• Clearly document solution architecture, model methodology, operational procedures, assumptions, limitations, and performance.

• Partner with Fraud Operations, Cyber Analytics, engineering teams, business stakeholders, and external technology providers.

• Provide technical guidance, mentor less-experienced team members, and promote reusable development and documentation standards.

  Requirements

• Minimum 6-10 years of experience in business intelligence, analytics, data engineering, or a related field.

  Basic Qualifications

• Bachelors degree in data science, computer science, engineering, statistics, mathematics, operations research, or a related quantitative field, or equivalent practical experience.

• 7 or more years of professional experience in data science, machine learning, analytics engineering, or a closely related discipline.

• Advanced proficiency in Python and SQL.

• Demonstrated experience developing and validating machine learning or statistical models.

• Experience with anomaly detection, classification, time-series analysis, risk scoring, or similar applied modeling techniques.

• Strong knowledge of feature engineering, model validation, performance measurement, and model monitoring.

• Experience designing and maintaining ETL or ELT pipelines using large, complex datasets.

• Experience developing maintainable production applications or analytical processes, rather than only exploratory models or notebooks.

• Experience integrating REST APIs or other service-based data sources.

• Experience with Git, code review, CI/CD, and production deployment practices.

• Experience working with cloud-based data or application services.

• Ability to independently troubleshoot issues across data, code, models, integrations, and production environments.

• Strong written and verbal communication skills, including the ability to explain technical concepts to nontechnical audiences.

  Preferred Qualifications

• Masters degree in quantitative or technical discipline.

• Experience with e-commerce fraud, payment risk, account abuse, transaction monitoring, chargebacks, or fraud case-management processes.

• Experience with Snowflake and cloud-native analytical architectures.

• Experience deploying applications or analytical services using Azure, AWS, Kubernetes, or container technologies.

• Experience with relational databases such as PostgreSQL.

• Experience with graph databases, particularly Neo4j and Cypher.

• Familiarity with MLOps, model-serving patterns, application monitoring, and observability tools.

• Experience integrating fraud, identity, payment, or risk-scoring APIs.

• Experience leading vendor proofs of concept or technical product evaluations.

• Familiarity with secure development, secrets management, authentication, vulnerability scanning, and enterprise change controls.

  Core Competencies

• Strong applied problem-solving and technical judgment

• Production-oriented data science and engineering discipline

• Ability to operate across data science, data engineering, and software development

• Understanding of operational workflows and business impact

• Clear communication with technical and nontechnical stakeholders

• Collaboration across functions, geographic locations, and time zones

• Technical leadership, mentoring, and knowledge sharing

  Role Expectations

The Lead Data Scientist will be expected to contribute meaningful components to the Cyber Fraud data science portfolio. Successful outcomes may include production fraud or anomaly-detection models, automated decisioning processes, third-party risk integrations, reliable data pipelines, relationship-based fraud analytics, and structured evaluations of new data or technology providers.

The ideal candidate is a hands-on senior technologist who can build innovative solutions while ensuring they are reliable, explainable, documented, supportable, and aligned with fraud-prevention objectives.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.