Business Intelligence Analyst

Data Engineer, Senior

VP - Payment Analytics

AVP – Evergreen Data Product Owner

AVP – Model Ops Engineer

Data Analyst Manager

Sourcing Support - Master Data

Financial Analyst

Manager, Data Science (GNS)

Responsibilities: <ul><li>Design, develop, and maintain dashboards and reports using Power BI and or Tableau</li><li>Connect to data sources including data warehouses (for example Redshift), ERP systems, and structured datasets</li><li>Prepare and transform data using Power Query, Tableau Prep, or similar tools</li><li>Build and maintain data models and calculations within BI tools to support scalable reporting</li><li>Translate business requirements into clear and effective visualizations and reports</li><li>Analyze data to identify trends, performance gaps, and opportunities for improvement</li><li>Ensure data accuracy by validating outputs against source systems and established definitions</li><li>Support user acceptance testing and incorporate feedback into reporting solutions</li><li>Maintain and enhance existing dashboards to improve usability and performance</li><li>Document key metrics, business logic, and report structures</li><li>Collaborate with cross-functional teams including business stakeholders and technical partners</li></ul> Requirements: <ul><li>Bachelor’s degree in Industrial engineering, Information Technology, Data Analytics, or related field</li><li>3 to 5 years of experience in Business Intelligence, Data Analytics, or Reporting roles</li><li>Experience developing dashboards using Power BI or Tableau</li><li>Experience working with enterprise data sources such as ERP systems or data warehouses</li><li>Experience supporting business or operational analytics, with supply chain exposure considered an advantage</li></ul>

Responsibilities: <ul><li>Design, develop and deploy process automation workflows to support Smart Factory operation.</li><li>Extract, transform and analyze data into accurate, meaningful and actionable reports to support operational and business decision making, such as yield reports with data visualizations and recommendations.</li><li>Play a role as a bridge between business, IT and vendor(s) to support new loaders, functionalities and enhancements for Yield Management System such as Discover Yield tools, Tableau dashboards as well as Advanced Yield Examination internal tools.</li><li>With Product and Process knowledge, ensure data integrity for YMS tools.</li><li>Combine statistics, machine learning and computer science to turn data into actionable insights for solving complex process issue(s).</li><li>Design, build and maintain Predictive AI Models to enable proactive decision-making.</li></ul> Requirements: <ul><li>Bachelor’s Degree in Computer Science or&nbsp;Electronic/Electrical Engineering, with min. 2 years’ experience in Data Analytics field.</li><li>Proficiency in SQL and experience working with relational databases.</li><li>Strong working knowledge of statistics and its applications.</li><li>Experience in building automated reports.</li></ul>

Responsibilities: <ul><li>Develop and execute a clear, actionable, and time-bound analytics roadmap, outlining key milestones, resources, and timelines to achieve payment analytics objectives.</li><li>Lead the design and delivery of comprehensive analytics across all payment channels, products, and features, providing end-to-end visibility into the payment lifecycle.</li><li>Identify, assess, and bridge data gaps by collaborating with business, technology, operations, and data engineering teams to ensure complete, accurate, and timely payment data capture.</li><li>Translate complex payment data into actionable insights and strategic recommendations that optimize the payment process, improve customer experience, and drive business growth.</li><li>Partner closely with Tech &amp; Ops Payments, Product Analytics, Payment Train, Customer Experience, and other key stakeholders to embed analytics into decision-making at all levels.</li><li>Build, mentor, and manage a high-performing analytics team, fostering a culture of innovation, accountability, and continuous learning.</li><li>Cultivate and maintain strong relationships across teams to effectively communicate analytics value and drive enterprise-wide adoption.</li><li>Support regulatory review and governance</li><li>Promote advanced analytics practices, including proof-of-concept initiatives, to keep the organization at the forefront of payment analytics capabilities.</li><li>Lead internal initiatives, perform other duties and/or special projects, as assigned</li></ul> Requirements: <ul><li>Degree in Statistics, Mathematics, Economics, Engineering, or any other quantitative streams with 10+ years of hands-on relevant analytics/data science experience, or in lieu of a degree 12+ years of relevant analytics/data science experience.</li><li>8+ years of hands-on Experience in SQL /SAS with proven ability to leverage advanced algorithms and be efficient in handling complex/large data;</li><li>3+ years of experience in BI tools, e.g. Tableau etc.</li><li>5+ years of experience in managing teams with ability to motivate team, drive change, create a culture of learning, innovation and recognition</li><li>Ability to solve complex technical and people-oriented problems with minimal supervision</li><li>Advanced proficiency in MS-Excel and MS-PowerPoint with superior data analyses and story boarding skills</li><li>Strong written/oral communication &amp; stakeholder management skills</li></ul>

Responsibilities: <ul><li>Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).</li><li>Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.</li><li>Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs.</li><li>Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.</li><li>Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).</li><li>Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.</li><li>Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&amp;A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs. Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts.</li><li>Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring</li><li>Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.</li></ul> Requirements: <ul><li>Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 7+ years’ experience OR, in lieu of degree, and 9+ years experience with program or product management experience leading complex, cross-functional data or analytics programs.</li><li>7+ years of of experience managing programs or products, including: 5+ years of experience defining business needs, prioritizing data and analytics solutions, and coordinating cross-functional technical execution and 3+ years of experience working in data integration, modeling, warehousing, or big data environments—preferably supporting modeling or advanced analytics.</li><li>Demonstrated experience leading Agile or Scaled Agile programs (SAFe) across cross-functional teams.</li><li>Proven track record of enabling model or analytics platforms, data-as-a-product delivery, or Model Lifecycle Operations/Data Operations functions.</li><li>Agile or Product Owner certification (e.g., CSPO, SAFe PO/PM).</li><li>Broad understanding of credit risk, fraud, marketing analytics, regulatory modeling (e.g., CECL, Basel), or consumer lending data environments.</li><li>Experience working with large-scale data ecosystems, including cloud (AWS, Azure), data lakes, and metadata management tools.</li></ul>

Responsibilities: <ul><li>Design, develop, and maintain robust pipelines to collect, transform, and store data used in model monitoring workflows (e.g., scoring data, performance metrics, outcomes).</li><li>Build scalable data architectures to support real-time and batch monitoring, including data ingestion, enrichment, and retention practices.</li><li>Develop reusable monitoring components (e.g., performance drift detectors, threshold-based alerts, metric repositories) that support various model types and regulatory needs.</li><li>Integrate data pipelines with model lifecycle platforms, MLOps tools, and observability solutions to ensure seamless model performance tracking.</li><li>Partner with model risk and compliance teams to ensure data lineage, audit trails, and documentation are preserved and accessible for regulatory reviews (e.g., SR 11-7 compliance).</li><li>Collaborate with data scientists, model validators, and product managers to align monitoring data infrastructure with evolving model monitoring requirements.</li><li>Work closely with the model monitoring analytics and strategy monitoring analytics teams within MO&amp;A to ensure the monitoring data infrastructure adapts to changing analytics and monitoring needs. Enable visualization and reporting capabilities through dashboards (e.g., Power BI, Tableau) that summarize model health, stability, and issue alerts.</li><li>Designing and maintaining high-performance data pipelines that ingest, transform, and version datasets for Model and Strategy Monitoring</li><li>Optimize data storage and compute performance for large-scale monitoring use cases involving high-frequency scoring or model ensembles.</li></ul> Requirements: <ul><li>Bachelor’s degree in a quantitative, technical, or data-focused field (e.g., Statistics, Mathematics, Computer Science, Data Science, Engineering) with 5+ years’ experience or in lieu of degree, and 7+ years of relevant work experience in, data engineering or related roles in the financial services or regulated analytics domain.</li><li>Strong proficiency with data engineering tools and frameworks (e.g., Apache Spark, Airflow, Kafka, dbt, PySpark).</li><li>Proficient in programming languages such as SAS, Python, and SQL for building monitoring pipelines and validation checks.</li><li>Experience with cloud-based data infrastructure (e.g., AWS, Azure, GCP) and data warehousing (e.g., Snowflake, Redshift, BigQuery).</li><li>Familiarity with MLOps practices, model metadata tracking (e.g., MLflow), and monitoring toolkits (e.g., Evidently AI, Why Labs, Prometheus).</li><li>Understanding of model risk governance requirements and the role of data engineering in ensuring compliant model monitoring.</li><li>Ability to work in an agile environment and deliver high-quality, production-grade code in collaboration with DevOps and platform engineering teams.</li></ul>

Responsibilities: Core Analytics &amp; Insight Generation: <ul><li>Lead advanced analysis across digital behavior, customer engagement, campaign performance, funnel conversion, and eCommerce/POS sales impact.</li><li>Transform complex datasets into clear insights and recommendations that improve marketing performance, segmentation, and content targeting.</li><li>Conduct market, customer, and competitive analytics to identify opportunities, threats, and whitespace for growth.</li><li>Develop and maintain data-driven customer segments, ICP models, behavioral profiles, and opportunity sizing frameworks.</li></ul> Data Products, Measurement &amp; Reporting: <ul><li>Own and continuously optimize dashboards, analytical models, and reporting tools that enable visibility into performance and ROI.</li><li>Define and implement measurement frameworks, attribution logic, KPI hierarchies, and performance scorecards across marketing channels.</li><li>Build scalable data products that improve the accuracy, availability, and usability of marketing and commercial datasets.</li></ul> Data Management &amp; Governance: <ul><li>Ensure data quality, consistency, and governance across marketing, CRM, web analytics, and eCommerce datasets.</li><li>Partner with BI, Digital Technology, and data engineering teams to optimize data pipelines, tagging/instrumentation, and data taxonomy.</li></ul> Cross-Functional Analytics Partnership: <ul><li>Collaborate with marketing, sales, eCommerce, and product teams to understand business questions and translate them into analytical solutions.</li><li>Present insights to stakeholders at various levels, guiding decision‑making with clear narratives and fact‑based recommendations.</li><li>Share analytics best practices and educate teams on interpreting data and leveraging insights for better outcomes.</li></ul> Requirements: Desired Skills &amp; Experience: <ul><li>Strong quantitative and analytical skills with experience in digital analytics, customer analysis, attribution, and commercial data.</li><li>Ability to translate data into strategic insights and communicate them clearly to both technical and non‑technical stakeholders.</li><li>Experience with web analytics tools (e.g., GA4, Adobe), BI platforms (Power BI/Tableau), and CRM/marketing automation systems.</li><li>Technical proficiency in SQL, data modeling, and large dataset manipulation (preferred).</li><li>Detail-oriented with a strong sense of ownership, accountability, and proactive problem solving.</li><li>Demonstrated ability to partner cross‑functionally and influence decision‑making through data.</li><li>Comfortable working in a dynamic environment with evolving priorities.</li></ul> Required Qualifications: <ul><li>12 years+ experience overall, with 6+ years of experience in analytics, data insights, digital analytics, marketing science, business intelligence, or related fields.</li><li>Analytics function leadership &amp; team management experience preferred.</li><li>Strong experience analyzing digital marketing data, customer behavior data, funnel metrics, and/or commercial revenue datasets.</li><li>Experience in B2B, digital marketing analytics, or eCommerce analytics is highly valuable but not required.</li><li>Proven ability to turn complex datasets into business-ready insights and clear narratives.</li><li>Hands-on expertise with:</li><li>Web analytics platforms (GA4, Adobe Analytics)</li><li>BI tools (Power BI, Tableau)</li><li>CRM and marketing automation systems (Salesforce, Marketo, Eloqua, etc.)</li><li>Comfortable working with large &amp; complex datasets.</li><li>Demonstrated experience influencing decision‑making in a cross-functional environment.</li><li>Detail-oriented, accountable, and proactive in identifying and addressing analytical needs.</li></ul>

Responsibilities: <ul><li>Establish and maintain data quality metrics and KPIs, regularly monitoring and reporting on data quality issues, trends, and improvements (also in VBA, SQL)</li><li>Collaborate with stakeholders, including supply chain planners, IT teams, and business leaders, to identify master data requirements and define data governance policies and procedures.</li><li>Lead the identification, collection, and validation of master data from various sources, ensuring data accuracy and integrity throughout the data lifecycle.</li><li>Define and implement data cleansing, enrichment, and standardization processes to improve data quality and usability.</li><li>Drive the adoption and utilization of master data management tools and systems, providing guidance and support to users, and ensuring data integrity and consistency across platforms.</li><li>Collaborate with IT teams to design, implement, and enhance master data management systems and infrastructure, aligning with business requirements and best practices.</li><li>Provide guidance and training to supply chain planning teams on master data management processes, standards, and tools, promoting data governance awareness and adherence.</li><li>Stay updated on industry trends, best practices, and emerging technologies related to master data management, and proactively identify opportunities for improvement and innovation.</li><li>Lead data-related projects and initiatives, coordinating cross-functional teams, managing timelines and deliverables, and ensuring successful project execution.</li></ul> Requirements: Education, Technical Qualification &amp; Experience: <ul><li>Bachelors degree in Supply Chain Management, Business Administration, Information Systems, or a related field.</li></ul> Professional Experience (years in what processes and function): <ul><li>Proven experience 1-2 years master data management, data governance, or a related field, preferably in a supply chain planning environment.</li><li>Strong knowledge of supply chain planning processes, including demand planning, inventory management, and production planning.</li><li>Proficiency in utilizing master data management tools and systems (e.g., SAP, Tableau, VBA, SQL.).</li><li>Solid understanding of data governance principles, data quality management, and data lifecycle management.</li></ul> Special Qualifications, Knowledge &amp; Skills: <ul><li>Experience with data analysis and reporting, using tools such as Excel, Tableau, or Power BI.</li><li>Excellent analytical and problem-solving skills, with a keen attention to detail. Need to be detail oriented and able to communicate to cross functional teams.</li><li>Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders.</li><li>Ability to work in a fast-paced and dynamic environment, managing multiple priorities and deadlines effectively.</li></ul>

Responsibilities: Responsible for ensuring timely and quality financial planning and reporting, including: <ul><li>IT Cost Reporting</li><li>Departmental Spending</li><li>IPRB / Rationalisation / Cost Saving</li><li>Financial Review package Scrap</li><li>Spending analysis for Actual vs Outlook vs Budget for various areas such as: Operations, Factory support, R&amp;D, S&amp;M, G&amp;A expenses, etc and identify opportunities for cost improvement.</li><li>Analyze financial results and highlight key trends, variances, and risks.</li><li>Provide insights into revenue, expenses, and profitability performance</li><li>Prepare Quarterly Outlook / Yearly Budget for:</li><li>Operations, Factory support, R&amp;D, S&amp;M, G&amp;A expenses forecast)IT spending</li><li>IPRB / Rationalisation / Cost Saving Scrap</li><li>Ad hoc projects - Process Automation / Improvement</li></ul> Requirements: <ul><li>Degree in Engineering / Accountancy/ Business Admin</li><li>2 to 3 years of Finance, Accounting, or related work experience.</li><li>Proficient in Microsoft office skill, preferably knowledge of SAP</li><li>Knowledge of VBA Excel Programming and Tableau application will be an added advantage.</li><li>Able to work under Stressful environment &amp; tight deadline</li><li>Good in planning and organizational and analytical skill</li><li>Proactive and good problem-solving skills</li><li>Possess good interpersonal and communication skills</li></ul>

Responsibilities: <ul><li>Manage a team of data scientists and engineers to design, develop, and deploy advanced AI solutions.</li><li>Lead the development of AI and machine learning models to solve business problems.</li><li>Research, build, and deploy models based on shallow and deep machine learning.</li><li>Train robust NLP-based models on large datasets.</li><li>Collaborate with cross-functional teams to align AI solutions with business goals and customer needs.</li><li>Design APIs for disseminating data science outputs.</li><li>Oversee the scaling of production systems to meet growing demands.</li><li>Working closely with other data scientists and engineers to design, develop, and deploy AI solutions.</li><li>Analyzing large datasets to identify patterns and trends that can inform AI development.</li><li>Communicating findings and insights to stakeholders in a clear and concise manner.</li><li>Staying current with the latest AI and machine learning trends and technologies.</li><li>Mentoring junior data scientists and provide guidance on AI and machine learning best practices.</li><li>Driving best practices across our data science functions.</li><li>Setting strategy and team direction, with a clear vision for Data Science.</li><li>Working with product leaders to apply data science solutions.</li><li>Evaluating and creating new frameworks and defining methodologies and governance.</li><li>Leading small- to medium-sized teams (direct or indirect).</li><li>Setting a high-bar for model development.</li></ul> Requirements: <ul><li>Have substantial experience in a senior data scientist role.</li><li>Have experience in leading complex projects and customer relationships.</li><li>Have significant experience in managing larger Technical teams (direct or indirect).</li><li>Have experience in mentoring or training others, acting as a subject matter expert to guide colleagues.</li><li>Be a natural problem solver, able to take a lead in collaborating to resolve issues.</li><li>Have excellent communication skills and the ability to convey information clearly to senior stakeholders.</li></ul> Highly Desired Skills and Experience: <ul><li>PhD or Masters degree in Computer Science, Mathematics, Statistics, or a related field.</li><li>7-10+ years of experience in AI and machine learning.</li><li>Deep understanding of machine learning algorithms, including deep learning, gradient boosting, and random forests.</li><li>Experience working directly with large language models and Transformer based architectures including BERT, RoBERTa, T5 etc.</li><li>Working knowledge of applying recent LLMs including ChatGPT, GPT 3.5, OPT, BLOOM, etc.</li><li>Experience with reinforcement learning, prompt engineering, hallucination mitigation.</li><li>Working understanding of the business risks associated with applying LLM in a business.</li><li>Experience working with large datasets and distributed computing systems (e.g., Hadoop, Spark).</li><li>Strong coding skills in Python or another programming language.</li><li>Excellent communicator, with the ability to translate complex technical concepts to non-technical stakeholders.</li><li>Strong problem-solving skills and the ability to think outside the box. </li></ul>

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9 tableau Jobs found

Business Intelligence Analyst

Data Engineer, Senior

VP - Payment Analytics

AVP – Evergreen Data Product Owner

AVP – Model Ops Engineer

Data Analyst Manager

Sourcing Support - Master Data

Financial Analyst

Manager, Data Science (GNS)

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Business Intelligence Analyst

Data Engineer, Senior

VP - Payment Analytics

AVP – Evergreen Data Product Owner

AVP – Model Ops Engineer

Data Analyst Manager

Sourcing Support - Master Data

Financial Analyst

Manager, Data Science (GNS)

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