• Transform complex datasets into actionable insights • Build and maintain analytics infrastructure • Partner with cross-functional teams to drive data-informed decision-making and product improvements • Own the end-to-end analytics lifecycle—from data modeling and dashboard creation to experimentation and KPI development

We are looking for a talented Quantum - Data Conversion Lead to join our team in Systems/Information Technology from your remote home office. In this role, you will make an impact in the following ways:  Own the outcome - Be the single owner for data readiness for each release, country, and plant—clean, complete, and on time. - Define the data conversion strategy, standards, and guardrails, and ensure consistent adoption. Plan & prioritize - Own the end‑to‑end data conversion plan (objects, cycles, dependencies, milestones). - Prioritize the conversion backlog with Build, Functional, and Test leads. - Align data loads with environment refreshes and testing cycles (SIT, E2E, UAT, PAT). Lead day‑to‑day execution - Review overnight data loads and conversion results; triage failures and data quality issues. - Direct daily work for data engineers and vendor SI partners to resolve root causes. - Ensure mappings, transformation rules, and reconciliation steps are accurate and repeatable. Coordinate across tracks - Partner with Integration Leads to align interfaces and conversions (structures, timing, cutovers). - Collaborate with Functional Leads to validate business rules and required data shapes. - Work with Testing teams to ensure clean, stable data is available and defects tied to data are resolved. Design governance - Lead reviews of conversion designs, mappings, and exception handling. - Enforce global standards for reuse, automation, error handling, and auditability. - Approve design changes that impact downstream processes, performance, or compliance. Quality & controls - Define and enforce Definition of Ready / Definition of Done for conversion objects. - Set and monitor data quality thresholds (accuracy, completeness, duplicates, referential integrity). - Ensure reconciliation and business sign‑offs are completed for each release. Issue management & decisioning - Make day‑to‑day trade‑off decisions (fix at source vs. transform vs. post‑load correction). - Escalate issues only when schedule, cost, compliance, or scope is materially impacted. - Drive root‑cause analysis for recurring issues and institutionalize permanent fixes. Cutover readiness - Own the data conversion runbook, timing, staffing, and contingency plans. - Validate mock conversions and ensure cutover rehearsals meet timing and quality targets. - Lead data activities during go‑live and support rapid stabilization. Stakeholder communication - Provide clear, concise status updates—what’s on track, what’s at risk, and recommended actions. - Translate technical data issues into business impact. - Communicate readiness, risks, and mitigation plans to program and leadership teams. People & partner leadership - Coach data engineers on scalable, reusable conversion patterns. - Manage vendor SI partners to outcomes and delivery commitments. - Build internal capability over time to reduce long‑term dependency on external partners. Compliance & auditability - Ensure data conversions comply with security, privacy, and retention requirements. - Maintain end‑to‑end data lineage and traceability from source to SAP. - Keep documentation current (mappings, rules, reconciliation templates, approvals). Continuous improvement - Industrialize the data conversion factory through automation and standardization. - Track KPIs such as pass rates, reconciliation variances, and cycle times. - Capture lessons learned and scale improvements globally. Cummins is an equal opportunity employer. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, sex, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity, or other status protected by law.

Alabang (96050), Philippines, Muntinlupa City, National Capital Region (Manila) Sr. Analyst, Data Analysis Summary: - As a Data Analyst, this role will leverage analytic and technical skills to innovate, build, and maintain well-managed data solutions and capabilities to tackle business problems. The role offers a unique opportunity to apply data-driven solutions in a fast-paced, risk-focused environment. You will develop dynamic reporting, advanced analytics, and automation to provide real-time risk management and drive efficiency. Leveraging cutting-edge technologies and innovative data practices, this role will play a key role in strengthening risk management strategies. General Responsibilities: - Innovation - Use Open Source/Digital technologies to mine complex, voluminous, and different varieties of data sources and platforms - Build well-managed data solutions, tools, and capabilities to enable self-service frameworks for data consumers - Demonstrate ability to explore and quickly grasp new technologies to progress varied initiatives - Business Intelligence - Partner with the business to provide consultancy and translate the business needs to design and develop tools, techniques, metrics, and dashboards for insights and data visualization - Drive analysis that provides meaningful insights on business strategies - Data Management - Drive an understanding and adherence to the principles of data quality management including metadata, lineage, and business definitions - Work collaboratively with appropriate Tech teams to manage security mechanisms and data access governance - Build and execute tools to monitor and report on data quality Basic Qualifications: - Educational Background: Bachelor's Degree in quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science or a related quantitative field) - Professional Experience: - At least 2 years of experience performing data analytics - At least 1 year of experience working with at least one scripting language - At least 1 year of experience utilizing a business intelligence visualization tool - At least 1 year of experience in querying and analyzing and working with data languages and platforms - Skills Required: - Strong desire and experience with data in various forms (data warehouses/SQL, unstructured data) - Experience utilizing and developing within AWS services Preferred Qualifications: - Master's Degree in a Science, Technology, Engineering, Mathematics discipline - At least 2 years of experience working within process management and improvement methodologies - Agile, Lean, Six Sigma, etc. - At least 1 year of experience utilizing and developing within AWS services - At least 1 year of experience working with and demonstrating knowledge of data governance, data quality management concepts and data quality tools (i.e. Informatica DQ) No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

• Data cleaning & preparation: Clean, recode, and structure incoming survey datasets - including applying advanced data quality checks and filters, raking & weighing, missing data, etc. • Conduct foundational data exploration including frequency distributions, cross-tabulations, and basic descriptive analyses, primarily in SPSS • Work fluently across survey data formats, principally SPSS (.sav) and R-native formats • Conduct advanced cluster analysis on complex, multi-country survey datasets, working hand in hand with the Head of Data & Research Methods regarding analytical decisions and final segmentation outputs • Evaluate and compare clustering approaches (e.g. k-means, hierarchical, latent class analysis, and others as appropriate) with a view to producing segments that are statistically robust, meaningful, and cross-nationally comparable • Manage the specific methodological challenges of complex survey data: dealing with varying variable types (nominal, ordinal, continuous), handling of translated or culturally non-equivalent items • Iteratively test and refine cluster solutions, systematically varying parameters and documenting the impact of each decision on outputs • Using existing, labelled segmentation outputs as a training base, design and fit (machine learning / train-test) an appropriate classification model to enable assignment of new respondents to established segments • Evaluate candidate classification approaches (e.g. random forest, logistic regression, LDA, gradient boosting, or others) and select the most appropriate given the data structure, segment separability, and intended use • Assess model performance rigorously using appropriate validation strategies (e.g. cross-validation, held-out test sets, confusion matrices, precision/recall) • Identify the minimum set of survey questions ('golden questions') that are most predictive of segment membership — i.e. those that would need to be included in future quantitative research instruments to allow reliable classification of new respondents • Apply appropriate variable importance and feature selection techniques to identify and rank candidate questions, and validate their predictive power • Produce clear recommendations on the golden question set, including supporting evidence and sensitivity analyses • Design and implement a practical classification tool or calculator that can be applied to future survey datasets to assign respondents to segments based on the golden question set • Maintain detailed records of all analytical iterations, including variations in parameters, model specifications, and the rationale behind decisions taken