Vuori, Inc
Carlsbad, California
Job Description Job Description Company Description Vuori is re-defining what athletic apparel looks like: built to move and sweat in but designed with a casual aesthetic to transition into everyday life. We draw inspiration from an active coastal California lifestyle; an integration of fitness, creative expression and life. Our high energy fast paced retail environment is reflected in the clothes we make. We aim to inspire others to take on all aspects of their lives with clarity, enthusiasm and purpose while having a lot of fun along the way. We are proud to be an outlet for opportunity and for personal growth and success. Job Description We're looking for a Sr. Data Modeler to design and govern the data models that serve as the foundation for analytics, reporting, and business intelligence across the organization - with particular depth in semantic and metrics-layer modeling. Beyond traditional schema design, you'll build the semantic layer that sits between raw data and downstream consumers (dashboards, reverse-ETL syncs, AI-assisted querying), working closely with data engineers, analysts, and business stakeholders to keep definitions consistent everywhere they're used. What you'll get to do: Contribute to the semantic layer strategy - defining metrics, dimensions, and business logic once so they're consistent across BI tools, reverse-ETL syncs, and AI-assisted querying Design serving-layer models as the "source of truth" for business definitions, resolving ambiguity before it reaches dashboards Build and govern semantic views/metrics layers (e.g., Snowflake semantic views, dbt metrics/semantic layer) that let analysts and AI tools query curated data without re-deriving business logic Partner directly with analytics and BI consumers to understand how models get used downstream, and iterate on semantic definitions based on real query patterns Reduce metric drift by auditing existing reports/dashboards for divergent definitions of the same business concept and consolidating them into governed semantic models Qualifications Who you are: 5+ years of experience in data modeling or a related field, including experience operating at a senior or technical lead level Experience designing or maintaining a semantic layer or metrics layer (e.g., dbt Semantic Layer, LookML, Snowflake semantic views, Cube) that serves multiple downstream consumers Strong point of view on the difference between a data model and a semantic model, and how to design for both correctness and analyst usability Advanced SQL skills and deep understanding of relational database design Experience modeling data within modern cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) Experience working with transformation frameworks such as dbt Strong understanding of data governance, metadata management, and documentation practices Excellent communication skills, with the ability to translate business needs into technical designs and explain technical concepts to non-technical stakeholders Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience preferred. Foundational background in dimensional modeling (Kimball and/or Inmon) - helpful context, though this role emphasizes semantic/metrics layer design over traditional schema work Experience with data catalog or governance tools (e.g., Collibra, Alation, Atlan) Familiarity with data mesh or data product concepts Background in a specific industry domain relevant to the business (e.g., finance, healthcare, retail) Additional Information Our investment in you: At Vuori, we're proud to offer the following to our employees: Health Insurance Savings and Retirement Plan Employee Assistance Program Generous Vuori Discount & Industry Perks Paid Time Off Wellness & Fitness benefits The salary range for this role is $137,300 per year - $176,610 per year. This role is bonus eligible. Vuori is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. All your information will be kept confidential according to EEO guidelines.
Job Description Job Description Company Description Vuori is re-defining what athletic apparel looks like: built to move and sweat in but designed with a casual aesthetic to transition into everyday life. We draw inspiration from an active coastal California lifestyle; an integration of fitness, creative expression and life. Our high energy fast paced retail environment is reflected in the clothes we make. We aim to inspire others to take on all aspects of their lives with clarity, enthusiasm and purpose while having a lot of fun along the way. We are proud to be an outlet for opportunity and for personal growth and success. Job Description We're looking for a Sr. Data Modeler to design and govern the data models that serve as the foundation for analytics, reporting, and business intelligence across the organization - with particular depth in semantic and metrics-layer modeling. Beyond traditional schema design, you'll build the semantic layer that sits between raw data and downstream consumers (dashboards, reverse-ETL syncs, AI-assisted querying), working closely with data engineers, analysts, and business stakeholders to keep definitions consistent everywhere they're used. What you'll get to do: Contribute to the semantic layer strategy - defining metrics, dimensions, and business logic once so they're consistent across BI tools, reverse-ETL syncs, and AI-assisted querying Design serving-layer models as the "source of truth" for business definitions, resolving ambiguity before it reaches dashboards Build and govern semantic views/metrics layers (e.g., Snowflake semantic views, dbt metrics/semantic layer) that let analysts and AI tools query curated data without re-deriving business logic Partner directly with analytics and BI consumers to understand how models get used downstream, and iterate on semantic definitions based on real query patterns Reduce metric drift by auditing existing reports/dashboards for divergent definitions of the same business concept and consolidating them into governed semantic models Qualifications Who you are: 5+ years of experience in data modeling or a related field, including experience operating at a senior or technical lead level Experience designing or maintaining a semantic layer or metrics layer (e.g., dbt Semantic Layer, LookML, Snowflake semantic views, Cube) that serves multiple downstream consumers Strong point of view on the difference between a data model and a semantic model, and how to design for both correctness and analyst usability Advanced SQL skills and deep understanding of relational database design Experience modeling data within modern cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) Experience working with transformation frameworks such as dbt Strong understanding of data governance, metadata management, and documentation practices Excellent communication skills, with the ability to translate business needs into technical designs and explain technical concepts to non-technical stakeholders Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience preferred. Foundational background in dimensional modeling (Kimball and/or Inmon) - helpful context, though this role emphasizes semantic/metrics layer design over traditional schema work Experience with data catalog or governance tools (e.g., Collibra, Alation, Atlan) Familiarity with data mesh or data product concepts Background in a specific industry domain relevant to the business (e.g., finance, healthcare, retail) Additional Information Our investment in you: At Vuori, we're proud to offer the following to our employees: Health Insurance Savings and Retirement Plan Employee Assistance Program Generous Vuori Discount & Industry Perks Paid Time Off Wellness & Fitness benefits The salary range for this role is $137,300 per year - $176,610 per year. This role is bonus eligible. Vuori is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. All your information will be kept confidential according to EEO guidelines.
Versant Media
New York, New York
Job Description Job Description Company Description VERSANT is a leading force in news, sports and entertainment - home to iconic and trusted brands that inspire, inform, and delight audiences. Our unique combination of content, technology and services enriches the cultural fabric, igniting passions, sparking conversations, and connecting people to what they love most. As an independent, publicly traded company, VERSANT brings together powerhouse cable networks - including USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!, SYFY, and Golf Channel - with dynamic digital and direct-to-consumer brands such as Fandango, Rotten Tomatoes, GolfNow, GolfPass, and SportsEngine. Together, these businesses reflect our commitment to delivering exceptional experiences across every screen and service. VERSANT is an industry-changing media company fueled by innovation and an entrepreneurial spirit. With a strong foundation and a forward-looking vision, VERSANT empowers creativity, embraces change, and drives connection in an ever-evolving world. Job Description We are seeking a Senior Data Modeler to define, evolve, and govern our enterprise-wide North Star Data Model. This role will make data consistent, interoperable, discoverable, and reusable across business domains, products, analytics, and AI use cases. The Senior Data Modeler will work with Product, domain experts, Data Engineering, Analytics, Architecture, Governance, and Security to translate business concepts into a shared enterprise data language and practical models that teams can implement across the Bronze, Silver, and Gold layers of our data platform. This is not a role focused only on designing tables for individual projects. It owns the connective tissue between domain models: the common entities, identifiers, relationships, definitions, metadata, and standards that allow trusted data products to work together across the enterprise. What you will do Establish the enterprise North Star Data Model Define and maintain enterprise conceptual, logical, and physical data models across core business domains. Establish canonical business entities, shared dimensions, reference data, identifiers, relationships, and lifecycle states. Create a pragmatic model that supports domain autonomy while enabling cross-domain analysis and data sharing. Maintain an enterprise ontology, business glossary, and semantic definitions so that important terms and metrics mean the same thing across products and teams. Design for reuse, extensibility, regional variation, and future use cases while avoiding unnecessary centralization. Model the Bronze, Silver, and Gold data layers Define modeling principles and required artifacts for each layer: Bronze: Preserve source fidelity, source lineage, ingestion metadata, auditability, and raw-data contracts. Silver: Standardize and validate data; apply common identifiers, canonical entities, conformed dimensions, data-quality rules, and cross-domain integration patterns. Gold: Deliver governed, business-ready data products, semantic models, certified metrics, and analytics-ready structures for dashboards, self-service analysis, and AI. Ensure traceability from Gold metrics and business concepts back through Silver transformations to authoritative Bronze sources. Define clear rules for when a concept belongs in a domain model, the shared enterprise model, a semantic layer, or a product-specific analytical model. Review and guide physical implementations for performance, maintainability, cost, privacy, and scale. Enable data products and delivery teams Partner with Product Managers and domain leaders to turn business outcomes and use cases into clear data-modeling requirements. Partner with Data Engineers to define schemas, transformations, mappings, data contracts, and implementation patterns. Partner with BI developers to create governed semantic models, reusable measures, and self-service-ready datasets. Facilitate architecture and design reviews; identify duplication, inconsistent definitions, broken lineage, and integration risk early. Provide model patterns, templates, and coaching that enable teams to deliver independently while following enterprise standards. Govern for trust, security, and global scale Embed data quality, lineage, ownership, retention, security classification, privacy-by-design, and access-control requirements into data-model designs. Model regional, regulatory, language, currency, and local-business variations without fragmenting global reporting or shared concepts. Define stewardship and decision rights for enterprise entities, metrics, and reference data. Maintain model documentation and metadata in the organization's data catalog and modeling tools. Measure adoption, reuse, quality, coverage, and exceptions to drive continual improvement of the North Star model. What success looks like Teams use the same definitions for shared business concepts and key metrics. New data products can be delivered faster because common models, dimensions, identifiers, and patterns are reusable. Cross-domain reporting is reliable, explainable, and traceable to authoritative sources. Data consumers can discover what data exists, what it means, who owns it, and whether it is fit for use. Regional expansion is supported through intentional extensions rather than isolated local models. Gold-layer analytics and AI experiences use certified semantic definitions rather than reconstructing logic independently. Qualifications Required qualifications 7+ years of experience in data modeling, data architecture, data engineering, analytics engineering, or related disciplines. Demonstrated experience designing conceptual, logical, and physical models for complex, multi-domain data platforms. Experience defining or evolving an enterprise data model, canonical model, semantic layer, ontology, or common data model. Strong understanding of dimensional modeling, normalized modeling, data vault or equivalent integration patterns, and lakehouse/warehouse design. Experience designing models for raw, standardized, and consumption-ready data layers in a modern cloud data platform. Proven ability to translate business concepts into unambiguous data definitions, models, mappings, and implementation guidance. Practical expertise in metadata, lineage, data quality, data governance, master/reference data, and data privacy. Strong facilitation and communication skills; able to influence senior stakeholders and delivery teams without relying on direct authority. Preferred qualifications Experience with Databricks, Delta Lake, Unity Catalog, Snowflake, or comparable cloud data platforms. Experience with a data catalog, modeling tool, and metadata-management platform. Experience supporting global products and multi-region data, regulatory, or data-residency requirements. Experience designing semantic models and governed metrics for BI, natural-language data experiences, or AI applications. Experience in a regulated, high-scale, or operationally sensitive environment. Additional Information As part of our selection process, external candidates may be required to attend an in-person interview with a VERSANT Media employee at one of our locations prior to a hiring decision. VERSANT Media's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law. If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to . VERSANT Media is committed to fair and equitable compensation practices. We include a good faith pay range for each position to comply with applicable state and local pay transparency laws and to promote equity across our organization. Actual compensation will be based on factors such as the candidate's skills, qualifications, experience, and location and may include additional forms of compensation and benefits such as health insurance, retirement plans, paid time off, etc. VERSANT Media is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at VERSANT via-email, the Internet, or in any form and/or method without a valid written Statement of Work in place for this position from VERSANT's Talent Acquisition team will be deemed the sole property of VERSANT. No fee will be paid in the event the candidate is hired by VERSANT as a result of the referral or through other means.
Job Description Job Description Company Description VERSANT is a leading force in news, sports and entertainment - home to iconic and trusted brands that inspire, inform, and delight audiences. Our unique combination of content, technology and services enriches the cultural fabric, igniting passions, sparking conversations, and connecting people to what they love most. As an independent, publicly traded company, VERSANT brings together powerhouse cable networks - including USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!, SYFY, and Golf Channel - with dynamic digital and direct-to-consumer brands such as Fandango, Rotten Tomatoes, GolfNow, GolfPass, and SportsEngine. Together, these businesses reflect our commitment to delivering exceptional experiences across every screen and service. VERSANT is an industry-changing media company fueled by innovation and an entrepreneurial spirit. With a strong foundation and a forward-looking vision, VERSANT empowers creativity, embraces change, and drives connection in an ever-evolving world. Job Description We are seeking a Senior Data Modeler to define, evolve, and govern our enterprise-wide North Star Data Model. This role will make data consistent, interoperable, discoverable, and reusable across business domains, products, analytics, and AI use cases. The Senior Data Modeler will work with Product, domain experts, Data Engineering, Analytics, Architecture, Governance, and Security to translate business concepts into a shared enterprise data language and practical models that teams can implement across the Bronze, Silver, and Gold layers of our data platform. This is not a role focused only on designing tables for individual projects. It owns the connective tissue between domain models: the common entities, identifiers, relationships, definitions, metadata, and standards that allow trusted data products to work together across the enterprise. What you will do Establish the enterprise North Star Data Model Define and maintain enterprise conceptual, logical, and physical data models across core business domains. Establish canonical business entities, shared dimensions, reference data, identifiers, relationships, and lifecycle states. Create a pragmatic model that supports domain autonomy while enabling cross-domain analysis and data sharing. Maintain an enterprise ontology, business glossary, and semantic definitions so that important terms and metrics mean the same thing across products and teams. Design for reuse, extensibility, regional variation, and future use cases while avoiding unnecessary centralization. Model the Bronze, Silver, and Gold data layers Define modeling principles and required artifacts for each layer: Bronze: Preserve source fidelity, source lineage, ingestion metadata, auditability, and raw-data contracts. Silver: Standardize and validate data; apply common identifiers, canonical entities, conformed dimensions, data-quality rules, and cross-domain integration patterns. Gold: Deliver governed, business-ready data products, semantic models, certified metrics, and analytics-ready structures for dashboards, self-service analysis, and AI. Ensure traceability from Gold metrics and business concepts back through Silver transformations to authoritative Bronze sources. Define clear rules for when a concept belongs in a domain model, the shared enterprise model, a semantic layer, or a product-specific analytical model. Review and guide physical implementations for performance, maintainability, cost, privacy, and scale. Enable data products and delivery teams Partner with Product Managers and domain leaders to turn business outcomes and use cases into clear data-modeling requirements. Partner with Data Engineers to define schemas, transformations, mappings, data contracts, and implementation patterns. Partner with BI developers to create governed semantic models, reusable measures, and self-service-ready datasets. Facilitate architecture and design reviews; identify duplication, inconsistent definitions, broken lineage, and integration risk early. Provide model patterns, templates, and coaching that enable teams to deliver independently while following enterprise standards. Govern for trust, security, and global scale Embed data quality, lineage, ownership, retention, security classification, privacy-by-design, and access-control requirements into data-model designs. Model regional, regulatory, language, currency, and local-business variations without fragmenting global reporting or shared concepts. Define stewardship and decision rights for enterprise entities, metrics, and reference data. Maintain model documentation and metadata in the organization's data catalog and modeling tools. Measure adoption, reuse, quality, coverage, and exceptions to drive continual improvement of the North Star model. What success looks like Teams use the same definitions for shared business concepts and key metrics. New data products can be delivered faster because common models, dimensions, identifiers, and patterns are reusable. Cross-domain reporting is reliable, explainable, and traceable to authoritative sources. Data consumers can discover what data exists, what it means, who owns it, and whether it is fit for use. Regional expansion is supported through intentional extensions rather than isolated local models. Gold-layer analytics and AI experiences use certified semantic definitions rather than reconstructing logic independently. Qualifications Required qualifications 7+ years of experience in data modeling, data architecture, data engineering, analytics engineering, or related disciplines. Demonstrated experience designing conceptual, logical, and physical models for complex, multi-domain data platforms. Experience defining or evolving an enterprise data model, canonical model, semantic layer, ontology, or common data model. Strong understanding of dimensional modeling, normalized modeling, data vault or equivalent integration patterns, and lakehouse/warehouse design. Experience designing models for raw, standardized, and consumption-ready data layers in a modern cloud data platform. Proven ability to translate business concepts into unambiguous data definitions, models, mappings, and implementation guidance. Practical expertise in metadata, lineage, data quality, data governance, master/reference data, and data privacy. Strong facilitation and communication skills; able to influence senior stakeholders and delivery teams without relying on direct authority. Preferred qualifications Experience with Databricks, Delta Lake, Unity Catalog, Snowflake, or comparable cloud data platforms. Experience with a data catalog, modeling tool, and metadata-management platform. Experience supporting global products and multi-region data, regulatory, or data-residency requirements. Experience designing semantic models and governed metrics for BI, natural-language data experiences, or AI applications. Experience in a regulated, high-scale, or operationally sensitive environment. Additional Information As part of our selection process, external candidates may be required to attend an in-person interview with a VERSANT Media employee at one of our locations prior to a hiring decision. VERSANT Media's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law. If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to . VERSANT Media is committed to fair and equitable compensation practices. We include a good faith pay range for each position to comply with applicable state and local pay transparency laws and to promote equity across our organization. Actual compensation will be based on factors such as the candidate's skills, qualifications, experience, and location and may include additional forms of compensation and benefits such as health insurance, retirement plans, paid time off, etc. VERSANT Media is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at VERSANT via-email, the Internet, or in any form and/or method without a valid written Statement of Work in place for this position from VERSANT's Talent Acquisition team will be deemed the sole property of VERSANT. No fee will be paid in the event the candidate is hired by VERSANT as a result of the referral or through other means.
Experian
Job Description Job Description Company Description Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries. Job Description Overview Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include strategic thinking, an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model. You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions. We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization. This is a remote role and you will report into the Sr. Manager of Fraud Analytics. What you'll do Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals. Work with senior data scientists to translate fraud questions into testable hypotheses Help build machine learning models for fraud detection across account opening, account takeover, and identity risk. Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction. Develop and validate features using identity, transactional, behavioral, and other available data sources. Write clean, well-tested code, and work with engineering to bring models and features into production. Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions. Help prepare analyses and communicate findings to both technical and nontechnical audiences. Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance. Qualifications 1+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection. Proficiency in Python, with the ability to write clean, readable, and well-tested code. Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn. Investigative mindset and the ability to move from unusual data patterns to testable hypotheses. Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools. Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work. Additional Information Benefits/Perks: Great compensation package and bonus plan Core benefits including medical, dental, vision, and matching 401K Flexible work environment, ability to work remote, hybrid or in-office Flexible time off including volunteer time off, vacation, sick and 12-paid holidays Explore all our exciting benefits here: -index.html Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning. We have won World's Best Workplaces 2025 (Fortune Global Top 25), and Great Place To Work in 26 countries to name a few. Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. Our compensation reflects the cost of labor across several U.S. geographic markets. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be eligible for a variable pay opportunity and a comprehensive benefits package. Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Job Description Job Description Company Description Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries. Job Description Overview Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include strategic thinking, an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model. You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions. We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization. This is a remote role and you will report into the Sr. Manager of Fraud Analytics. What you'll do Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals. Work with senior data scientists to translate fraud questions into testable hypotheses Help build machine learning models for fraud detection across account opening, account takeover, and identity risk. Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction. Develop and validate features using identity, transactional, behavioral, and other available data sources. Write clean, well-tested code, and work with engineering to bring models and features into production. Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions. Help prepare analyses and communicate findings to both technical and nontechnical audiences. Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance. Qualifications 1+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection. Proficiency in Python, with the ability to write clean, readable, and well-tested code. Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn. Investigative mindset and the ability to move from unusual data patterns to testable hypotheses. Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools. Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work. Additional Information Benefits/Perks: Great compensation package and bonus plan Core benefits including medical, dental, vision, and matching 401K Flexible work environment, ability to work remote, hybrid or in-office Flexible time off including volunteer time off, vacation, sick and 12-paid holidays Explore all our exciting benefits here: -index.html Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning. We have won World's Best Workplaces 2025 (Fortune Global Top 25), and Great Place To Work in 26 countries to name a few. Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. Our compensation reflects the cost of labor across several U.S. geographic markets. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be eligible for a variable pay opportunity and a comprehensive benefits package. Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
