Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Division Manager - VP2

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Senior Manager – Technology Transformation

Supplier Development Process Engineer

Head of Card Operations

Finance Analyst III (Plant Operations)

Responsibilities: Deploy and Land: <ul><li>Own end-to-end delivery of AI and analytics products at stations in your region.</li><li>Cover discovery, scoping, development, integration, user acceptance testing (UAT), hypercare and rollout.</li><li>Embed with operations.</li><li>Participate in business stand-ups.</li><li>Visit the ramp and cargo floor.</li><li>Understand local data realities rather than making assumptions.</li><li>Ship production code in Python and comparable stacks against live flight, cargo, workforce and equipment data.</li><li>Avoid handing over prototypes for someone else to productionise.</li><li>Scale deployment by reducing the cost and time required to deploy solutions across additional stations.</li><li>Achieve this through reusable integrations, configuration and runbooks.</li></ul> Build on the Platform, Not Around It: <ul><li>Consume data through the semantic layer and governed data platform.</li><li>Avoid parallel pipelines, local definitions and workarounds.</li><li>Apply Centre of Excellence (CoE) patterns and guardrails.</li><li>Follow reference architectures, AI registry, human-in-the-loop controls, access and residency rules.</li><li>Integrate GenAI and ML into workflows.</li><li>Implement RAG and retrieval patterns.</li><li>Implement agentic and tool-use patterns.</li><li>Apply evaluation frameworks.</li><li>Establish observability.</li><li>Maintain production cost control.</li></ul> Make Insight Self-Service: <ul><li>Build data products, not report queues.</li><li>Design governed, reusable datasets and semantic models.</li><li>Enable operations teams, airlines and forwarders to answer their own questions.</li><li>Put insight in front of the customer.</li><li>Deliver station and customer-facing analytics.</li><li>Enable natural-language access to operational data.</li><li>Ensure appropriate access, residency and confidentiality controls.</li><li>Design for the user, not the requester.</li><li>Ensure every product identifies the decision it changes and the person responsible for making it.</li><li>Instrument usage.</li><li>Retire products that are not used.</li><li>Consolidate fragmented reporting rather than extending it.</li></ul> Close the Loop: <ul><li>Feed field learnings back.</li><li>Convert integration problems into hardened patterns for the CoE and requirements for the platform team.</li><li>Own adoption and benefits.</li><li>Instrument deployments.</li><li>Track usage and benefits against the business case.</li><li>Report results accurately.</li><li>Be the technical face to the business and customers.</li><li>Translate between operations, the CoE and platform engineering.</li><li>Success in the First 12 Months</li></ul> Requirements: Experience: <ul><li>6+ years of experience in software or ML engineering.</li><li>Experience must include customer-facing or business-embedded delivery.</li><li>Demonstrated track record of scoping and delivering complex systems end to end in ambiguous, fast-moving environments.</li><li>Ability to remain accountable after go-live.</li><li>Production experience with LLM or ML systems.</li><li>Experience with prompt and agent design.</li><li>Experience with RAG.</li><li>Experience with evaluation frameworks.</li><li>Experience with deployment at scale.</li><li>Understanding of how model behaviour influences product experience.</li><li>Strong Python and API development skills.</li><li>Experience with FastAPI or equivalent.</li><li>Comfortable working across backend and frontend development.</li><li>Ability to build a working system independently.</li><li>Practical experience with Docker.</li><li>Practical experience with Kubernetes.</li><li>Practical experience with CI/CD.</li><li>Practical experience with cloud data platforms.</li><li>Experience integrating batch and real-time data against complex enterprise data sources.</li><li>Strong understanding of analytics.</li><li>Experience with semantic and dimensional modelling.</li><li>Knowledge of modern BI.</li><li>Experience designing data products that non-technical and external users can operate independently.</li><li>Aviation, logistics or complex operations experience is advantageous but not mandatory.</li><li>Willingness to learn operations directly from the operational floor is essential.</li></ul> Qualifications: <ul><li>Bachelors, MSc or PhD in Computer Science, Engineering, Mathematics or a related field.</li></ul>

Responsibilities: <ul><li>Own end-to-end delivery of AI and analytics products at stations in your region: discovery, scoping, build, integration, UAT, hypercare and rollout.</li><li>Embed with the operation. Sit in business stand-ups, walk the ramp and the cargo floor, and specify the local data reality rather than assuming it.</li><li>Ship production code in Python and comparable stacks against live flight, cargo, workforce and equipment data, not prototypes handed to someone else to productionise.</li><li>Scale deployment. Reduce cost and time-to-deploy from station N to station N+1 through reusable integrations, configuration and runbooks.</li><li>Consume through the semantic layer and the governed data platform. No parallel pipelines, no local definitions, no workarounds.</li><li>Apply CoE patterns and guardrails: reference architectures, AI registry, human-in-the-loop controls, access and residency rules.</li><li>Integrate GenAI and ML into workflows: RAG and retrieval, agentic and tool-use patterns, evaluation frameworks, observability and cost control in production.</li><li>Build data products, not report queues. Design- governed, reusable datasets and semantic models that let operations, airlines and forwarders answer their own questions.</li><li>Put insight in front of the customer. Deliver station and customer-facing analytics, including natural-language access to operational data, with the access, residency and confidentiality controls customer data demands.</li><li>Design for the user, not the requester. Every product names the decision it changes and who makes it. Instrument usage, retire what nobody opens, consolidate the long tail rather than extend it.&nbsp;</li><li>Feed the field back. Every integration problem you solve becomes a hardened pattern for the CoE and a requirement for the platform team.</li><li>Own adoption and benefits. Instrument what you deploy, track usage and benefit against the business case, and report it honestly.</li><li>Be the technical face to the business and to customers, translating between operations, the CoE and platform engineering.&nbsp;</li></ul> Requirements:&nbsp; <ul><li>6+ years in software or ML engineering, including customer-facing or business-embedded delivery.</li><li>Track record of scoping and shipping complex systems end to end in ambiguous, fast-moving environments, and staying accountable after go-live.</li><li>Production experience with LLM or ML systems: prompt and agent design, RAG, evaluation frameworks, deployment at scale, and an understanding of how model behaviour shapes product experience.</li><li>Strong Python and API development (FastAPI or equivalent); comfortable across backend and front-end enough to build a working system alone.</li><li>Practical depth in Docker, Kubernetes, CI/CD, and cloud data platforms; batch and real-time integration against messy enterprise sources.</li><li>Fluency in the analytics half of the job: semantic and dimensional modelling, modern BI, and designing data products that non-technical and external users can operate without help.</li><li>Aviation, logistics or complex operations exposure is an advantage, not a prerequisite. Willingness to learn the operation from the floor is not optional.</li><li>Bachelors, MSc or PhD in Computer Science, Engineering, Mathematics or related field.&nbsp;</li></ul>

Responsibilities: <ul><li>Lead the engineering, operation, and continuous improvement of enterprise cloud platforms across Microsoft Azure (primary), AWS, and GCP, including landing zones, networking, platform services, and secure cloud foundations.</li><li>Build and maintain standardized delivery pathways, CI/CD pipelines, infrastructure-as-code (IaC) modules, GitOps practices, and environment automation to enable consistent, scalable, and compliant deployments.</li><li>Drive an automation-first engineering model, reducing manual intervention through policy-as-code, automated workflows, AI-assisted engineering, and agent-based execution of routine operational activities.</li><li>Develop and mature AIOps capabilities, including anomaly detection, intelligent alert correlation, automated remediation, and measurable reduction of operational toil.</li><li>Design and operate infrastructure supporting enterprise AI workloads, including GPU/accelerated compute, managed AI services, model serving and inference endpoints, vector and retrieval services, and secure AI landing zones.</li><li>Partner with Data &amp; AI teams to provide scalable, secure infrastructure for AI solutions while managing capacity, quotas, workload isolation, and AI infrastructure consumption and cost reporting.</li><li>Lead cloud reliability, observability, and resilience, establishing service level objectives (SLOs), error budgets, metrics, logging, tracing, recovery playbooks, and validated RTO/RPO requirements.</li><li>Implement cloud security, identity, and compliance guardrails through secure baselines, policy-as-code, workload identities, privileged access controls, and Microsoft Entra ID integration.&nbsp;</li><li>Maintain platform compliance with applicable data protection and industry standards, including GDPR, CCPA, PCI-DSS, and SOC 2, in partnership with Global Cybersecurity and other governance teams.</li><li>Execute FinOps and cloud optimization initiatives, including tagging and cost allocation, rightsizing, scheduling, storage optimization, reservations, savings plans, committed spend, forecasting, and consumption reporting.</li><li>Support the organizations cloud-first transformation, including modernization and consolidation of VMware workloads and rationalization of AWS and GCP environments around strategic business requirements.</li><li>Lead and develop a globally distributed cloud engineering team, establishing engineering standards, structured code and design reviews, operational discipline, continuous learning, and accountability for cloud providers and engineering partners.</li></ul> Requirements: <ul><li>Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent professional experience.</li><li>10+ years of progressive cloud and infrastructure engineering experience, including at least 3 years leading engineering teams with accountability for production platform services.</li><li>Expert-level, hands-on experience with Microsoft Azure, with demonstrated experience building and operating environments across AWS and GCP.</li><li>Deep experience with platform engineering and infrastructure-as-code, including Terraform, Bicep, GitOps, CI/CD pipelines, landing zones, and automated provisioning.</li><li>Demonstrated experience implementing cloud automation and AI-enabled operations, including AIOps, AI-assisted engineering, or agent-based automation.</li><li>Hands-on experience supporting AI/ML infrastructure, including GPU/accelerated compute, managed AI services, model serving, or AI workload platforms.</li><li>Strong production experience with Kubernetes (AKS, EKS, or GKE), cloud security, identity, observability, and reliability practices.</li><li>Experience leading distributed engineering teams and supporting highly available, business-critical technology environments.</li></ul> Preferred Skills <ul><li>Professional certifications such as Azure Solutions Architect Expert, AWS Certified Solutions Architect – Professional, Google Professional Cloud Architect, CKA, Terraform Associate, or FinOps Certified Practitioner.</li><li>Experience with Microsoft Entra ID, workload identity, privileged access, Conditional Access, zero-trust principles, and policy-as-code.</li><li>Experience with observability and SRE practices, including Grafana, Prometheus, Azure Monitor, CloudWatch, SLOs, and error budgets.</li><li>Working knowledge of FinOps, cloud cost optimization, tagging strategies, reservations, savings plans, and consumption reporting.</li><li>Experience migrating and modernizing VMware and on-premises workloads into cloud environments.</li><li>Experience leading global teams across the Americas, EMEA, and APAC.</li><li>Experience within aviation, transportation, logistics, field operations, or other 24/7/365 operational environments.</li></ul>

Responsibilities: Service Operations Management: <ul><li>Lead and manage the First Recovery (L1 Application Support) team comprising approximately 19 analysts.</li><li>Ensure seamless delivery of 16x6 operational support (Monday through Saturday) and weekend coverage during Mainframe and AS400 IPL activities.</li><li>Drive effective handling of incidents, service requests, alerts, and operational tasks.</li><li>Ensure incidents are prioritized, triaged, and assigned appropriately to meet SLA commitments.</li><li>Act as the primary escalation point for critical incidents and major service disruptions.</li></ul> Incident &amp; Request Management: <ul><li>Drive achievement of the teams key KPI of maintaining a minimum 60% First Recovery Resolution Rate for all incidents received by the L1 support team.</li><li>Monitor incident trends and recurring issues, initiating preventive and corrective actions.</li><li>Ensure timely escalation of unresolved incidents to L2/L3 support teams.</li><li>Conduct incident reviews and implement lessons learned to improve service quality.</li></ul> Application &amp; Infrastructure Health Monitoring: <ul><li>Oversee proactive application and infrastructure health checks.</li><li>Ensure monitoring activities are performed consistently and operational issues are detected early.</li><li>Work with infrastructure, middleware, database, and application teams to improve system stability and availability.</li><li>Drive automation and operational improvements where appropriate.</li></ul> Knowledge Management: <ul><li>Establish and maintain a comprehensive Knowledge Base (KB) repository.</li><li>Ensure all recurring incidents, service requests, troubleshooting procedures, and operational runbooks are documented.</li><li>Promote knowledge sharing and continuous learning within the team.</li><li>Drive reduction of repeat incidents through effective documentation and process improvements.</li></ul> People Leadership: <ul><li>Manage, coach, and develop a team of 15–20 direct reports.</li><li>Perform resource planning, scheduling, workload distribution, and succession planning.</li><li>Conduct performance management, mentoring, and career development activities.</li><li>Foster a culture of accountability, customer focus, collaboration, and continuous improvement.</li><li>Ensure adequate staffing and capabilities are maintained to support business requirements.</li></ul> Stakeholder Management: <ul><li>Engage with application owners, infrastructure teams, business users, and senior management on operational matters.</li><li>Provide regular reporting on KPIs, incident trends, service improvements, and team performance.</li><li>Participate in governance meetings and operational reviews.</li></ul> Continuous Improvement: <ul><li>Identify opportunities to improve operational processes, tooling, and monitoring capabilities.</li><li>Drive reduction in incident volumes through root cause analysis and preventive actions.</li><li>Support operational transformation initiatives and service maturity improvements.</li></ul> Requirements: <ul><li>Bachelors Degree in Information Technology, Computer Science, Engineering, or a related discipline.</li><li>Minimum 8–12 years of IT experience with at least 3–5 years in a people management role.</li><li>Proven experience managing 15–20 direct reports in an Application Support environment.</li><li>Strong background in L1 and/or L2 Application Support.</li><li>Experience managing shift-based operational teams.</li><li>Demonstrated experience in Incident Management, Service Request Management, and Operational Support.</li></ul>

Responsibilities: <ul><li>Gather and analyze user requirements for dashboard, monitoring, and reporting enhancements.</li><li>Design, develop, and maintain operational dashboards and monitoring solutions based on business and technical requirements.</li><li>Create, modify, and optimize queries to extract, analyze, and present data for business and operational reporting.</li><li>Develop reports and analytics that provide actionable insights into system performance, availability, and operational health.</li><li>Support and administer enterprise monitoring tools such as Splunk and Geneos.</li><li>Troubleshoot monitoring alerts, dashboard discrepancies, and reporting-related issues.</li><li>Participate in system implementation, enhancement, testing, and deployment activities.</li><li>Ensure compliance with organizational standards, governance, and audit requirements.</li><li>Prepare and maintain technical documentation, user guides, and operational procedures.</li><li>To identify area of monitoring coverage to be improved; and work with relevant teams to enable monitoring solution to improve monitoring coverage.</li><li>To identify area of triage efficiency to be improved; and work with relevant teams to implement solution to improve triage efficiency.</li><li>To identify area of automation of service recovery ability to be improved; and work with relevant teams to implement solution to automate service recovery ability.</li><li>To work with various stakeholders (i.e., service automation, level 2 (group) and level 3 production support etc) to implement application monitoring, service end-to-end dashboard, and automation of service recovery.</li></ul> Requirements: <ul><li>Strong hunter mentality with a passion for creating new business opportunities and opening new partner relationships.</li><li>Experience in business development, channel sales, partner sales, technology sales, or customer acquisition roles.</li><li>Comfort with outbound prospecting, cold outreach, networking, pipeline generation, and opportunity development.</li><li>Understanding of cloud technologies, managed services, software licensing, cybersecurity, or broader IT solutions.</li><li>Strong commercial acumen with the ability to influence stakeholders and build strategic business relationships.</li><li>Self-motivated, resilient, and driven by achieving growth targets, partner acquisition goals, and business outcomes.</li></ul>

Role Description: <ul><li>The organization is investing in a small number of high-impact AI and data products intended to operate across its global station network. </li><li>Each requires a single accountable owner who can hold the technical architecture and the product direction together, from first implementation through multi-station scale.</li><li>We are looking for a Lead, AI &amp; Data Products to take that ownership.&nbsp;</li><li>This is a senior individual contributor role for an engineer or applied scientist who has moved into product leadership, not a product manager with adjacent technical exposure.</li><li>You will be expected to reason about model design, data architecture and production trade-offs at the level of the engineers building the system, and to hold the roadmap, business case and stakeholder relationships at the level of the executives funding it.&nbsp;</li><li>The role operates horizontally.</li><li>You will act as the single-threaded owner for assigned initiatives, working with the AI Centre of Excellence on patterns and feasibility, with Data Engineering and Platform on the data foundation, with Operational Excellence and the business product owners on process change, and with the forward deployed engineers in region on station rollout.</li></ul> This is a hands-on role. <ul><li>You will review and contribute to code, make architectural calls, and spend meaningful time at the frontline understanding operations directly rather than through documentation.</li></ul> Responsibilities: Technical product ownership <ul><li>Own the product end to end for assigned AI and data initiatives: discovery, technical architecture, roadmap, delivery sequencing and global rollout readiness.</li><li>Act as single-threaded owner. One person accountable for whether the product works, is adopted, and delivers its business case across stations and regions.</li><li>Convert programs into products.&nbsp;</li><li>Move initiatives currently run as projects onto a product footing with a roadmap, a lifecycle, named users and a benefit measure.</li><li>Make the build decisions: in-house, CoE pattern, capability center, or vendor. Own the trade-off and the consequences.</li></ul> Technical depth <ul><li>Set the architecture for AI and data products spanning forecasting, optimization, simulation, GenAI and agentic workflows, and defend it in technical review.</li><li>Work in the codebase. Review designs and pull requests, prototype where it resolves ambiguity faster than discussion, and contribute directly when delivery depends on it.</li><li>Own production quality: model robustness, explainability, evaluation, monitoring, latency and cost in service, not only at launch.</li><li>Build on the platform. Consume governed data through the semantic layer and apply CoE guardrails rather than constructing parallel capability.</li></ul> Business and delivery leadership <ul><li>Translate operational problems into technical solutions and, equally, translate technical constraints into terms operations and commercial leadership can act on.</li><li>Own the business case with the business product owner: the benefit hypothesis, the measurement approach and the reported outcome.</li><li>Lead without direct authority across engineering, data science, operations and regional teams, including delivery through the capability center and vendor partners.</li><li>Work from the frontline.</li><li>Regular Gemba at stations, with operational understanding built firsthand.</li></ul> Requirements: <ul><li>10+ years in engineering, applied AI or data science, with a substantial portion spent building and running systems in production rather than specifying them.</li><li>Demonstrable record of owning technical products end to end and scaling them beyond a first implementation into multi site or multi-market operation.</li><li>Depth in one or more of: forecasting (time series, probabilistic, hierarchical), optimization (MIP, LP, heuristics), simulation, or production GenAI and agentic systems, with working literacy across the others.</li><li>Strong Python and SQL, with practical command of the modern data and ML stack: cloud data platforms, pipelines, model deployment, evaluation and monitoring.</li><li>Experience influencing senior stakeholders and operating across engineering, operations and commercial functions without direct reporting lines.</li><li>Aviation, logistics or complex physical operations exposure is an advantage, not a prerequisite.</li></ul> Qualifications <ul><li>Bachelors, MSc or PhD in Computer Science, Engineering, Data Science, Mathematics, Operations Research or a related field. Personal characteristics</li><li>Technical credibility earned through building, not through managing builders.</li><li>Judgement on trade-offs between scope, speed and quality, with the confidence to decide under incomplete information.</li><li>Clear communication with engineers, operators and executives, adjusting register without losing precision.</li><li>Comfort with ambiguity and being accountable for outcomes rather than activity.</li></ul>

Role Description: <ul><li>The role holder identifies and defines opportunities, builds the business case for each initiative, and leads delivery through internal technology functions and delivery partners. Initiatives are held to their intended outcomes, adoption and benefits, not go-live alone. This is a hands-on role. </li><li>The role holder personally designs, builds and deploys data, analytics, automation and AI solutions, and coordinates multidisciplinary teams.</li></ul> Responsibilities: <ul><li>Provide analysis and recommendations to help develop DHI’s technology, data and digital transformation agenda, for review by the Executive Director – Portfolio Operations and Intelligence.</li><li>Develop and maintain roadmaps for assigned initiatives, sequencing activity by value, dependency, risk and readiness.</li><li>Lead discovery and solution design with DHI departments, running workshops that define the opportunity, scope and intended outcome of each initiative.</li><li>Identify opportunities to improve revenue, productivity, customer experience, operational performance and decision-making through data, analytics, automation and AI.</li><li>Define value hypotheses, success metrics, adoption targets and value realisation frameworks for each initiative.</li><li>Design, develop, deploy and maintain data and analytical solutions, dashboards, automation solutions and AI-enabled products.</li><li>Build and maintain ETL/ELT processes and data transformation workflows.</li><li>Own the lifecycle of data, analytics, automation and AI products, from concept through deployment and adoption.</li><li>Define product vision, user requirements, roadmaps, backlogs and delivery priorities, based on the end-to-end needs of alternative asset management users, including investment professionals, portfolio managers, senior leadership and governance functions. Translate business challenges into technical requirements, user stories and solution designs.</li><li>Lead end-to-end delivery of assigned transformation initiatives, managing scope, schedule, cost and quality.</li><li>Ensure initiatives meet cybersecurity, data protection, privacy and AI governance requirements.</li></ul> Requirements: <ul><li>6 – 8 years of experience in technology transformation, data product development, applied AI, technology consulting or data-enabled transformation.</li><li>Demonstrated experience personally taking a problem through to a production-grade product.</li><li>Bachelor’s degree in Computer Science, Engineering, Data Science, AI, Mathematics, Statistics, Business or another relevant quantitative discipline.</li><li>Advanced degree, such as an MBA, preferred.</li><li>Technology, data and digital transformation planning and roadmap development</li><li>Data platforms such as Databricks, Azure AI Foundry, Spark, Hive or Palantir Foundry</li></ul>

Responsibilities: Cross function collaboration: <ul><li>Coordinate Procurement, SQE, PDE, MPDE, Engineering Assess and document manufacturing capability, process maturity, and QMS compliance during PCBA new supplier qualification/Development.</li><li>Engage upstream and downstream raw material partners to improve delivery capability, capacity alignment, and supplier responsiveness.</li><li>Organize and lead regular cross-functional reviews with PDE, MPDE, and suppliers to address delivery risks, manufacturing readiness, and improvement actions.</li><li>Track and drive closure of cross-functional and supplier action items, escalating gaps as needed.</li></ul> NPI stage: <ul><li>Provide early manufacturing and engineer support for PCBA commodities from a sourcing and supply chain perspective to support supplier selection decisions (critical).</li><li>Conflict Management: Coordinate and facilitate DFM (Design for Manufacturing) discussions with suppliers, and Engineering to ensure manufacturing requirements are clearly understood and accepted (no design decision ownership).</li><li>Assess and document manufacturing capability, process maturity, and QMS compliance during PCBA new supplier qualification.</li><li>Act as a coordination interface between Procurement and Engineering, ensuring alignment on quality, commercial, and technical expectations without assuming design ownership.</li></ul> Approved Supplier Management: <ul><li>Drive yield, process stability, and delivery performance improvements at supplier sites.</li><li>Collaborate with suppliers and key supply chain nodes to improve lead time and delivery reliability, supporting company sample and production needs.</li><li>Partner with CM and Procurement to support SRM programs for strategic PCBA suppliers, with close tracking of action plan execution.</li><li>Facilitate cross-functional supplier improvement workshops (e.g. CoPQ reduction, lead time optimization) as a neutral coordinator, ensuring clear ownership and accountability.</li><li>Monitor progress and escalate unresolved issues where action items are not closed as committed.</li></ul> Requirements: <ul><li>Bachelor’s degree in Electrical / Automation/ Mechanical or any Engineering related majors.</li><li>At least 7 years of MPDE experience within PCBA (Industry relevant), connector and cable assembly Quality Management portion. Procurement, supply chain experience OR Total years of experience 10+ years in various industries including PCBA.</li><li>Knowledge on specific product and process as required (Connectors, Cable and Cable Assemblies, PCBA Tolerances and Cost Drivers)&nbsp;</li><li>In-depth knowledge of electronic manufacturing processes such as PCBA assembly (SMT/THT), box build, testing, and packaging</li><li>Understanding of supplier capabilities, capacity planning, and EMS operating models</li><li>Familiarity with DFM/DFA (Design for Assembly), component sourcing, and BOM management</li><li>Awareness of quality systems, certifications (ISO 9001,IATF 16949 and IPC- Association Connecting Electronics Industries), and related compliance standards</li><li>Ability to work with EMS partners on cost, yield, lead time, and continuous improvement</li><li>Experience supporting NPI, supplier qualification, and scale-up to mass production</li><li>Expert level with the tools related to supplier APQP process, include but not limited to PPAP, FMEA, CP, MSA, SPC, R@R and 8D methodology is a Plus.</li><li>Thorough knowledge of Problem-Solving tools (5-Why, Ishikawa/Fishbone diagrams, Fault Tree</li><li>Analysis)</li></ul>

Responsibilities: <ul><li>Lead the COPC team</li><li>Lead and responsible for day-to-daye operations to ensure SLAs and all deliverables are met</li><li>Streamline processes to reduce operating cost and turnaround time without compromising quality and risk.&nbsp;</li><li>Conduct internal reviews / tests to ensure compliance</li><li>Assist with data analysis, report findings, recommend corrective actions and preventive actions for review.&nbsp;&nbsp;</li><li>Proactively manage and resolve customers’ and business units’ feedback and queries promptly</li><li>Drive operations related projects &amp; process improvements</li><li>Lead and coach staff to a high standard of performance, productivity and service quality</li><li>Ensure that the Bank’s policies, guidelines, operations control and compliance requirements are strictly adhered to;</li><li>Ensure that operation procedures are kept up to-date;</li><li>Identify opportunities to improve productivity, efficiency and service quality in the team and within the division while assuming the role as a mentor to coach staff to ensure high standard of performance, productivity and service quality</li><li>Candidate must come from banking industry and done / doing cards operations. This is to manage total HC of 70 (incl TL &amp; Dep TL).</li></ul> Requirements: <ul><li>Bachelor’s degree</li><li>At least 10 years of retail banking operations, especially in Credit/Debit Cards Operations&nbsp;</li><li>At least 10 years of team management experience with a minimum size of 20</li><li>Able to multi-task and cope with change and diversity in a fast-paced environment</li><li>Experience in change management projects, including successful RPA and automation implementations.</li><li>Strong organizational and management skills with strong leadership qualities</li><li>Strong analytical and problem-solving skills</li><li>Strong interpersonal and communication skills with excellent verbal/written communication and interpersonal skills</li><li>Ability to think objectively and think outside the box when analyzing issues</li><li>Ability to work under pressure</li></ul>

Responsibilities: Planning and Budgeting&nbsp;: <ul><li>Assist on annual budget preparation.&nbsp;</li><li>Assist on monthly forecast preparation.&nbsp;</li><li>Track weekly forecast variance. In order to provide accurate and timely reports.&nbsp;&nbsp;</li></ul> Inventory Risk tracking/Analysis&nbsp;: <ul><li>Inventory reserve preparation&nbsp;</li><li>High light inventory risks and opportunities monthly&nbsp;</li><li>Monitor inventory physical count.&nbsp;</li><li>In order to comply with corporate policies and risk control&nbsp;&nbsp;</li></ul> Costing&nbsp;accounting&nbsp;: <ul><li>Data prepare(Mater master/Routine/Std cost);&nbsp;</li><li>Month ending JE booking&nbsp;</li><li>PPV program and analysis&nbsp;</li><li>Mat. usage analyzing&nbsp;&nbsp;</li><li>Production scrap report preparing&nbsp;&nbsp;</li></ul> FA accounting&nbsp;: <ul><li>Familiar with Capital Assets policy and comply with the policy(Include local policy)；&nbsp;</li><li>Prepare FA management report(Acquisitions, disposal, transfer etc.) and leading fixed&nbsp;</li><li>assets physical count&nbsp;</li><li>Issuing Asset Tags to assets responsible department&nbsp;</li><li>Intercompany Transfers of Fixed Assets data preparing and follow to financial posting&nbsp;</li><li>Follow Open PO&amp;IO&amp;CIP tracking report by month&nbsp;</li><li>CER review and budget control with report and management.&nbsp;</li></ul> Requirements: <ul><li>At least bachelor’s degree in finance/accounting/taxation or related.&nbsp;</li><li>Over 7-10 years working experience in Finance, costing managerial position is preferred&nbsp;&nbsp;</li><li>Good knowledge and experience in costing of manufacturing.&nbsp;Understand how to manage&nbsp;</li><li>and improve Mfg. variance.&nbsp;&nbsp;</li><li>Good knowledge and experience in SAP.&nbsp;</li><li>Fluent English Mandarin, both written and oral (Added Advantage)</li><li>Strong will to learn and develop the skills.&nbsp;</li></ul>

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165 production head Jobs found

Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Division Manager - VP2

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Senior Manager – Technology Transformation

Supplier Development Process Engineer

Head of Card Operations

Finance Analyst III (Plant Operations)

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Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Division Manager - VP2

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Senior Manager – Technology Transformation

Supplier Development Process Engineer

Head of Card Operations

Finance Analyst III (Plant Operations)

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