For years, digital health has been framed as&nbsp;the future of healthcare.By 2026, it feels different. Not like a breakthrough year, but a reset.After rapid experimentation, pandemic-driven acceleration, and heavy investment in new tools, the industry is entering a more disciplined phase. The question is no longer&nbsp;what technology can do, but&nbsp;what actually works at scale.<h2>From Speed to Accountability</h2>The 2020–2023 wave demonstrated that healthcare can rapidly adopt digital tools. Telehealth, remote monitoring, analytics, and AI scaled faster than anyone expected.But speed came with trade-offs.By 2026, healthcare leaders are asking harder questions:<ul><li>Does this solution improve patient outcomes?</li><li>Can clinicians rely on it in their daily workflows?</li><li>Is it maintainable, secure, and compliant long-term?&nbsp;</li><li>Does it integrate cleanly with existing systems?</li></ul>It’s telling that&nbsp;over 60% of healthcare executives now cite “demonstrating ROI” as their top digital challenge, ahead of adoption itself. The focus has shifted from deploying technology to proving its value.<h2>The End of Endless Pilots</h2>Another clear sign of reset: patience for endless pilots is running out.Healthcare organizations are consolidating vendors, platforms, and experiments. Instead of dozens of proofs-of-concept, they are prioritizing fewer initiatives with clear ownership, measurable impact, and long-term roadmaps.This shift is not theoretical.&nbsp;Nearly half of digital health initiatives never move beyond pilot or early-stage deployment, pushing organizations to rethink how and with whom they innovate.<h2>AI Matures-Execution Becomes the Differentiator</h2>AI remains central to digital health strategies, but it is no longer the headline.By 2026, access to AI is widespread. What differentiates organizations is execution:<ul><li>How AI is embedded into clinical and operational workflows</li><li>How models are governed, validated, and monitored</li><li>How explainability and regulatory readiness are handled</li></ul>The conversation has moved from&nbsp;what the model can do to&nbsp;how safely and reliably it performs in real care settings.<h2>Interoperability Becomes Non-Negotiable</h2>Interoperability has long been promised. In 2026, it will become mandatory.Healthcare systems can no longer afford siloed data or disconnected platforms. Digital health strategies are now judged by how well they connect clinical, operational, and financial data across ecosystems.API-first, standards-based architectures are no longer “nice to have”—they are baseline expectations.<h2>From Digital Experience to Real Clinical Impact</h2>Earlier phases of digital health emphasized apps, portals, and dashboards. While experience still matters, 2026 prioritizes impact.Technology is expected to:<ul><li>Reduce clinician workload</li><li>Improve decision-making</li><li>Shorten the time to treatment</li><li>Deliver measurable operational value</li></ul>The reset reflects a deeper understanding that transformation happens in systems, workflows, and data, not just interfaces.<h2>A Healthier Phase for Digital Health</h2>This reset is not a slowdown. It is a sign of maturity.Digital health in 2026 is becoming more focused, more realistic, and more outcome-driven. Organizations that succeed will not be those that adopt the most technology, but those that build strong foundations and execute with clarity.In that sense, a reset may be exactly what digital health needs.

Why 2026 Feels Like a Reset Year for Digital Health

why-2026-feels-like-a-reset-year-for-digital-health

Turning Complex Financial Data into Real-Time Market IntelligenceIn an increasingly data-driven financial ecosystem, organizations must process and interpret vast volumes of unstructured information—from market reports and economic indicators to regulatory updates and financial news. Advanced analytics and AI are essential to transform this data into timely, reliable intelligence that supports strategic decision-making.VMO delivers an enterprise-grade AI-powered financial data intelligence platform designed to automate data analysis, generate real-time insights, and enhance risk awareness across financial and economic domains.<h2 style="text-align:justify;">The Solution Approach</h2>VMO designed and implemented a comprehensive AI-driven platform that automates the end-to-end financial data intelligence lifecycle.The platform is built to handle large-scale, unstructured financial data and transform it into structured, actionable insights through:<ul><li>Data ingestion, cleaning, and normalization to ensure accuracy and consistency</li><li>Advanced feature extraction from financial documents, market movements, and economic data</li><li>AI-driven analytics to identify trends, risks, and market signals</li><li>Real-time insight delivery via secure API integrations with existing enterprise systems</li></ul>By leveraging AI and machine learning, the platform enables organizations to move from manual, fragmented analysis to continuous, intelligent financial monitoring.<h2 style="text-align:justify;">Key Capabilities</h2><ul><li>Automated processing of unstructured financial documents</li><li>Real-time market, economic, and regulatory insight generation</li><li>News and sentiment analysis for enhanced market awareness</li><li>AI-powered trend detection and risk prediction</li><li>Continuous monitoring of regulatory and policy updates</li><li>Seamless API-based integration with financial systems and tools</li></ul><h2 style="text-align:justify;">Business Impact</h2>The AI-powered financial data intelligence platform enables organizations to:<ul><li>Enhance financial intelligence through more accurate trend and risk analysis</li><li>Automate data-intensive processes, reducing reliance on manual analysis</li><li>Access real-time insights to support faster, more informed decisions</li><li>Improve operational efficiency and reduce analysis-related costs</li><li>Strengthen compliance monitoring through automated regulatory tracking</li></ul><h2 style="text-align:justify;">Technology Highlights</h2><ul><li>AI and machine learning models optimized for financial data analysis</li><li>Natural Language Processing (NLP) for document understanding and sentiment analysis</li><li>Scalable, cloud-based architecture</li><li>Secure, enterprise-grade API integrations</li><li>High-performance data pipelines supporting real-time analytics</li></ul><h2 style="text-align:justify;">Why VMO</h2>This solution showcases VMO’s expertise in building&nbsp;enterprise-scale AI platforms for complex data environments. By combining advanced analytics, AI-driven automation, and seamless system integration, VMO helps organizations unlock the full value of their financial data—transforming information complexity into strategic advantage.

AI-Powered Financial Data Intelligence Platform

ai-powered-financial-data-intelligence-platform

<h2>Why AI Documentation Becomes a Bottleneck at Enterprise Scale</h2>Many organizations have already moved beyond pilot AI use cases. They deploy speech-to-text for clinical notes, AI assistants for internal knowledge, or automated documentation across operations. Yet as AI adoption grows, documentation often becomes the hidden bottleneck.Common challenges we see at scale include:<ul><li>Fragmented documentation workflows across teams and systems</li><li>Inconsistent quality, tone, and structure of AI-generated content</li><li>Compliance and audit risks due to missing traceability</li><li>Low trust from end users who must verify or correct outputs</li><li>Rising operational costs as volume increases</li></ul>At scale, AI documentation is no longer a feature—it is an operational capability.<h2>What “Working at Scale” Really Means</h2>Making AI documentation work at scale requires more than deploying a model. It means the solution can:<ul><li>Handle high volume and concurrency without performance degradation</li><li>Integrate seamlessly with EHRs, enterprise systems, and workflows</li><li>Maintain consistency across departments, geographies, and use cases</li><li>Meet regulatory and security requirements by design</li><li>Adapt and improve as business rules, languages, and processes change</li></ul>This is where many off-the-shelf AI tools fall short.<h2>The Enterprise-Ready AI Documentation Stack</h2>Successful large-scale deployments typically rely on a layered architecture:<h3>1. Workflow-First Design</h3>AI documentation must fit into existing workflows, not force users to change how they work. This includes:<ul><li>Event-driven triggers (meetings, consultations, handovers)</li><li>Human-in-the-loop review where required</li><li>Clear ownership and approval paths</li></ul><h3>2. Domain-Aware Intelligence</h3>Generic language models struggle with domain nuance. At scale, documentation systems must be:<ul><li>Tuned with domain-specific vocabularies</li><li>Guided by structured templates and rules</li><li>Context-aware (role, department, patient, project)</li></ul><h3>3. Governance and Compliance Built In</h3>For regulated industries such as healthcare, finance, and insurance, AI documentation must support:<ul><li>Full audit trails and versioning</li><li>Data residency and access control</li><li>Explainability of AI-generated outputs</li></ul><h3>4. Continuous Learning Loop</h3>Scalable systems improve over time by:<ul><li>Learning from user edits and feedback</li><li>Monitoring accuracy, coverage, and turnaround time</li><li>Updating prompts, rules, or models without disruption</li></ul><h2>From Tool to Platform: A Shift in Mindset</h2>Organizations that succeed treat AI documentation as a platform capability, not a standalone tool. This enables:<ul><li>Reuse across multiple departments and use cases</li><li>Centralized governance with localized customization</li><li>Predictable cost models as volume grows</li><li>Faster rollout of new AI-enabled documentation scenarios</li></ul>This platform approach is often the difference between pilot success and enterprise-wide impact.<h2>How VMO Helps Organizations Scale AI Documentation</h2>At VMO, we help enterprises move from fragmented AI experiments to scalable, production-grade documentation platforms.Our approach typically includes:<ul><li>Designing end-to-end AI documentation workflows aligned with business processes</li><li>Building secure, compliant architectures tailored to regulated environments</li><li>Customizing AI models with domain data and structured logic</li><li>Integrating with existing systems such as EHRs, CRM, and internal knowledge bases</li><li>Establishing governance, monitoring, and continuous optimization</li></ul>The result is AI documentation that teams trust, compliance teams approve, and leadership can scale confidently.<h2>The Takeaway</h2>If your organization is already using AI for documentation but struggling with quality, consistency, or scale, the problem is unlikely the model. It is the system around it.The next step is not more AI features; it is an enterprise-grade documentation platform designed for scale.Ready to make AI documentation work across your organization? Let’s talk about how to turn your AI documentation from a productivity experiment into a scalable capability.

Why AI Documentation Becomes a Bottleneck at Enterprise Scale

Making AI Documentation Work at Scale

making-ai-documentation-work-at-scale

<h2 style="text-align:justify;">AI-Powered Monitoring System for Logistics &amp; Warehouse Operations</h2><h3 style="text-align:justify;">Using Computer Vision, QR Code &amp; Object Detection</h3>In complex&nbsp;logistics and warehouse environments, even a few minutes of uncontrolled handling can lead to significant operational risks. As operations scale across multiple storage and distribution facilities, manual monitoring becomes increasingly unreliable, creating blind spots in time-sensitive processes, especially in environments requiring strict handling control.This case study illustrates how&nbsp;VMO applied Computer Vision, QR Code recognition, and Object Detection to build an&nbsp;AI-powered monitoring system for logistics and warehouse operations, enabling real-time visibility, precise time tracking, and automated alerts—transforming a critical operational challenge into a fully automated, data-driven workflow.<h3 style="text-align:justify;">Challenge</h3>In&nbsp;large-scale logistics, warehouse, and storage operations, maintaining strict control over the time goods spend outside designated zones is critical. Manual monitoring methods often fall short due to limited visibility, delayed response, and a high risk of human error.The challenge was to build an&nbsp;intelligent, automated system capable of:<ul><li>Detecting when goods move across defined storage boundaries</li><li>Tracking the exact duration each item remains outside its assigned zone</li><li>Alerting operators in real time when predefined time thresholds are exceeded</li></ul>All of this needed to operate&nbsp;continuously, accurately, and at scale across complex warehouse environments.VMO SolutionVMO developed an&nbsp;end-to-end AI-driven monitoring solution for warehouse and logistics operations that integrates:<ul><li>A network of&nbsp;strategically installed cameras to capture real-time movements</li><li>A&nbsp;cloud-based video management platform for centralized processing and storage</li><li>An&nbsp;AI image analysis engine powered by: <ul><li>Computer Vision</li><li>Object Detection</li><li>QR Code recognition</li><li>OCR technology</li></ul></li></ul>The system automatically identifies individual items, detects entry and exit events, and precisely calculates exposure time outside designated zones—without requiring manual intervention.<h3 style="text-align:justify;">Key Capabilities</h3><ul><li>24/7 autonomous monitoring across logistics and warehouse areas</li><li>Real-time detection of item movement and zone transitions</li><li>Precise time tracking at the individual item level</li><li>Smart alerting when time limits are exceeded</li><li>Comprehensive activity logs for traceability and compliance support</li></ul><h3 style="text-align:justify;">Business Value</h3>The solution delivered tangible operational improvements for logistics and warehouse operations:<ul><li>Reduced reliance on manual checks and supervision</li><li>Minimized risk of time-related handling issues</li><li>Improved response speed through real-time alerts</li><li>Increased data accuracy and operational transparency</li><li>Enabled audit-ready tracking with complete historical records</li></ul><h3 style="text-align:justify;">Technology Highlights</h3><ul><li>Scalable cloud architecture</li><li>AI models optimized for&nbsp;logistics and industrial environments</li><li>Seamless integration with existing operational workflows</li></ul><h3 style="text-align:justify;">Why VMO</h3>This case demonstrates VMO’s ability to design and deploy&nbsp;practical AI solutions for logistics and warehouse operations. By combining&nbsp;Computer Vision, Object Detection, and cloud-based intelligence, VMO helps organizations transform complex operational monitoring challenges into&nbsp;automated, reliable, and scalable systems.

In complex logistics and warehouse environments, even a few minutes of uncontrolled handling can lead to significant operational risks. As operations scale across multiple storage and distribution facilities, manual monitoring becomes increasingly unreliable, creating blind spots in time-sensitive processes, especially in environments requiring strict handling control.

AI-Powered Monitoring System for Logistics & Warehouse Operations

ai-powered-monitoring-system-for-logistics-warehouse-operations

<h3>Why the next generation of clinical documentation is about intelligence, not transcription</h3>For many healthcare organizations, AI documentation still means one thing: voice-to-text.But transcription alone does not solve clinician burnout, fragmented workflows, or rising administrative burden. As healthcare becomes more digital and data-driven, documentation must evolve from a typing tool into an intelligent clinical assistant.AI documentation today is no longer just about capturing words. It’s about understanding care.<h2>The Problem With Traditional Voice-to-Text</h2>Voice-to-text systems focus on one narrow function: converting speech into text. While helpful, they still leave clinicians with a long list of manual tasks:<ul><li>Editing and structuring notes</li><li>Coding diagnoses and procedures</li><li>Mapping content to EHR templates</li><li>Ensuring compliance and audit readiness</li><li>Extracting insights from unstructured data</li></ul>As a result, clinicians still spend&nbsp;1–2 hours per day correcting, formatting, and completing documentation, often after clinic hours.The root problem isn’t typing speed. It’s&nbsp;cognitive load.<h2>What “AI Documentation” Really Means in 2026</h2>Next-generation AI documentation platforms go far beyond transcription. They operate as real-time clinical copilots.Instead of just writing what the clinician says, modern AI systems can:<h3>1. Understand Clinical Context</h3>AI models trained on medical language understand:<ul><li>Symptoms vs. diagnoses</li><li>Past medical history vs. current complaints</li><li>Medication changes vs. refills</li><li>Assessment vs. plan</li></ul>This allows the system to automatically structure notes into SOAP, H&amp;P, or specialty-specific templates.<h3>2. Generate Clinically Meaningful Summaries</h3>Rather than dumping long transcripts into the EHR, AI can generate:<ul><li>Visit summaries</li><li>Discharge notes</li><li>Referral letters</li><li>Progress notes</li></ul>All written in clinician-ready language: concise, structured, and compliant.<h3>3. Auto-Code and Support Billing</h3>Modern AI documentation engines can:<ul><li>Suggest ICD-10 and CPT codes</li><li>Flag missing documentation elements</li><li>Improve audit readiness</li><li>Reduce claim rejections</li></ul>This transforms documentation from a cost center into a revenue optimization tool.<h3>4. Integrate Across Clinical Systems</h3>True AI documentation is deeply integrated with:<ul><li>EHR platforms</li><li>Lab systems</li><li>Imaging systems</li><li>Pharmacy systems</li><li>Remote patient monitoring platforms</li></ul>This enables AI to pull relevant data automatically and reduce repetitive data entry.<h3>5. Learn From Clinical Behavior</h3>Advanced platforms continuously learn from:<ul><li>Clinician editing patterns</li><li>Specialty workflows</li><li>Institutional documentation standards</li></ul>Over time, documentation becomes faster, more accurate, and more personalized.<h2>The Business Impact for Healthcare Organizations</h2>When AI documentation is implemented as an intelligent platform, not just a dictation tool, healthcare organizations see measurable impact:<ul><li>30–50% reduction in documentation time</li><li>Higher clinician satisfaction and retention</li><li>Faster patient throughput</li><li>Improved coding accuracy</li><li>Better data quality for analytics and AI initiatives</li></ul>More importantly, it creates a foundation for future AI use cases such as predictive care, population health, and clinical decision support.<h2>The Architecture Behind Intelligent Documentation</h2>Delivering this level of capability requires more than an AI model. It requires a robust digital architecture:<ul><li>Secure clinical data pipelines</li><li>Real-time speech and NLP engines</li><li>Medical-grade LLMs</li><li>EHR integration layers</li><li>Compliance and audit frameworks</li><li>Scalable cloud infrastructure</li></ul>This is where healthcare-grade engineering matters.<h2>Looking Ahead</h2>By 2026, the most advanced healthcare organizations will no longer ask:“Do we use voice-to-text?”They will ask:“How intelligent is our documentation platform?”Because in modern healthcare, documentation is not just a record of care. It is the digital backbone of clinical operations.And the smarter it becomes, the more time clinicians get back to focus on what matters most: their patients.<h3>About VMO</h3>At VMO (an ALTEN company), we work with healthcare organizations globally to design and build secure, scalable AI platforms, from clinical documentation systems to data and AI foundations that power the next generation of digital healthcare.If you're exploring how AI can transform clinical workflows beyond transcription, we’re always open to exchanging perspectives.

AI Documentation Is More Than Voice-to-Text

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Clinician burnout is often described as a crisis of workload, staffing, or emotional strain. But one of the earliest and most overlooked contributors sits quietly inside everyday clinical routines:&nbsp;documentation.At the top of the funnel, this conversation isn’t about technology or solutions yet. It’s about awareness and understanding&nbsp;why documentation has become such a powerful stressor in modern healthcare.<h2>The Daily Reality Behind the Screen</h2>Across hospitals and clinics, documentation has steadily expanded beyond clinical notes.Today, clinicians spend a significant portion of their time:<ul><li>Clicking through forms and templates</li><li>Re-entering the same information across systems</li><li>Documenting for billing, audits, and compliance</li><li>Finishing notes after clinic hours</li></ul>These tasks rarely feel optional, and rarely feel clinical.What was meant to support care has slowly become one of the most time-consuming parts of the job.<h2>When Documentation Becomes Emotional Fatigue</h2>Burnout doesn’t arrive suddenly. It builds through repeated friction.Documentation contributes to this by:<ul><li>Interrupting clinical thinking with constant data entry</li><li>Reducing meaningful face-to-face time with patients</li><li>Creating pressure to “catch up” after hours</li><li>Making clinicians feel evaluated by completeness rather than care quality</li></ul>Over time, this erodes motivation and professional satisfaction, especially for clinicians who entered healthcare to treat people, not manage screens.<h2>A Shared Experience Across Healthcare Roles</h2>This challenge isn’t limited to physicians.Nurses, pharmacists, therapists, and care coordinators all face similar documentation pressures. Each role may use different systems, but the pattern is the same: fragmented workflows, duplicated effort, and growing administrative load.Burnout, in this sense, is not an individual issue. It’s a shared experience shaped by how healthcare systems operate.<h2>Why This Matters for Healthcare Leaders</h2>At an organizational level, documentation-driven burnout has ripple effects:<ul><li>Lower staff engagement</li><li>Higher turnover risk</li><li>Reduced quality of patient interaction</li><li>Increased likelihood of errors caused by fatigue</li></ul>Understanding documentation as a burnout trigger is the first step toward addressing clinician well-being in a meaningful way.<h2>What’s Next in This Conversation</h2>This article is the starting point, not the conclusion.In upcoming posts on the&nbsp;VMO blog, we’ll explore:<ul><li>How documentation workflows break down in real clinical settings</li><li>Why traditional EHR optimizations often fall short</li><li>The role of workflow redesign and automation in reducing admin load</li><li>How emerging approaches, including AI-assisted documentation, are reshaping clinician experience &nbsp;</li></ul>If clinician burnout, care quality, and sustainable healthcare delivery matter to you, this is a conversation worth following.<h2>Stay Connected</h2>Clinician burnout doesn’t start with exhaustion. It starts quietly with one note, one form, and one late night at a time.Follow the VMO blog as we continue unpacking this challenge and sharing practical, system-level perspectives on how documentation can shift from a burden back into a support for care.&nbsp; &nbsp;

Clinician Burnout Starts with Documentation

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<ul><li>Participate in outsourcing projects for clients in the Japanese market</li><li>Work directly with Japanese clients</li><li>Plan project activities, track progress, and manage onsite team members</li><li>Perform other tasks as assigned</li></ul>

AWS Engineer (onsite in Japan)

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Our project works on operations-technology in which integrates advanced security, technology, facilities management, customer service and talent to build and operate bespoke solutions for complex, critical operations that extend beyond security.We are looking for a Senior AI/ML Engineer in the AI Development Group, you will lead the rapid prototyping, development, and integration of AI capabilities across current and next-generation technology platforms. This role is pivotal in advancing the AI adoption journey, making AI a core driver of innovation and operational excellence. You will work closely with cross-functional teams to explore, experiment, and productize AI solutions, embedding advanced AI technologies into software and hardware ecosystems to enhance security, automation, and operational intelligence.What You'll Do<ul><li>Lead the rapid prototyping of AI solutions to enhance product offerings and operational capabilities.</li><li>Own the technical development of AI projects, from ideation to productization, deployment and maintenance.</li><li>Drive the integration of AI technologies into existing and future technology platforms, ensuring that AI enhances the efficiency, security, and scalability of solutions.</li><li>Oversee the development of reusable and scalable AI modules / services to enhance organizational agility and reduce development time for future projects.</li><li>Engage with stakeholders across departments to identify areas where AI can drive value and address business challenges.</li><li>Ensure AI solutions are compliant with industry standards, regulatory frameworks, and data privacy policies.&nbsp;</li><li>Champion AI best practices across the organization, promoting AI literacy and fostering a culture of AI-driven innovation.</li></ul>

Senior AI/ML Engineer

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About the Role<ul><li>We are seeking an AI Researcher to develop advanced, AI-driven computer vision systems for real-time defect monitoring in Laser Powder Bed Fusion (L-PBF) additive manufacturing. The role focuses on detecting, classifying, and quantifying microscopic and macroscopic defects during the powder re-coating and laser melting phases to ensure part quality and safety.</li><li>You will work at the intersection of machine learning, computer vision, and advanced manufacturing, translating research concepts into deployable, real-time solutions.</li></ul>Problem Statement<ul><li>During the L-PBF process, defects such as porosity (holes), hopping/recoil instabilities, powder residue, and streaking can occur and compromise part integrity. The objective of this role is to design and implement an AI-based monitoring system that can: Detect &amp; Classify defects in real time during powder spreading and laser melting Quantify Severity of detected flaws and trigger alerts when thresholds are exceeded</li></ul>Key Responsibilities<ul><li>Design and develop computer vision and deep learning models for real-time defect detection in L-PBF processes</li><li>Research and implement methods to classify defect types (e.g., porosity, hopping, powder residue, streaking)</li><li>Develop algorithms to quantify defect severity and confidence levels</li><li>Integrate AI models with in-situ sensor data (optical, thermal, high-speed imaging, or melt-pool monitoring systems)</li><li>Optimize models for low-latency, real-time inference on edge or industrial systems</li><li>Collaborate with manufacturing, materials, and hardware teams to validate models on real production data</li><li>Evaluate system performance using statistical and manufacturing-relevant metrics</li><li>Document research findings and contribute to patents, publications, or internal technical reports</li></ul>

AI ENGINEER (AI Researcher)

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We are looking for an experienced Senior Data Scientist with years of Life Insurance experience to join our fast-paced, high-energy team. As a Senior Data Scientist, you will perform quantitative data analysis to enable organizational decision making and develop solutions to complex business problems and create value to the business. This position offers exposure to a wide variety of analytics tools and technologies as well as unique challenges in problem-solving. Building and deploying predictive models in support of our pricing strategy will be a focus area.In This Role, You Will<ul><li>Synthesize data to uncover inherent trends, assess impact of data on business usage, and to make recommendations for improvement.</li><li>Build GLM pricing models in support of various property and casualty insurance products.</li><li>Research, recommend, and implement statistical and other mathematical methodologies appropriate for the given business problem.</li><li>Build explanatory and forecasting models using descriptive and inferential statistics, and regression analysis.</li><li>Perform data validation, preprocessing, and exploratory data analyses.</li><li>Communicates analytical goals, approach, outcomes, and insights to both technical and non-technical audiences.</li><li>Translate analytical findings into relevant business insights.</li><li>Collaborate with business partners and peers within the organization to understand and scope the problem, gather business requirements, and develop robust model solutions that drive improvement in key business metrics.</li><li>Effectively communicate with key stakeholders (both technical and non-technical) in written, oral and presentation formats.</li><li>Create/Maintain excellent working relationships with business partners across the Chubb organization including Product, Actuarial, IT, and analytics peer groups.</li><li>Provide guidance and mentorship directly and indirectly to junior team members.</li></ul>

AI Engineer (Life Insurance experience)

ai-engineer-life-insurance-experience

<ul><li>Tham gia các dự án product/outsource cho khách hàng Nhật Bản: lĩnh vực tài chính, y tế, ecommerce…&nbsp;</li><li>Kỹ sư cầu nối cho đội phát triển với khách hàng Nhật - Làm việc trực tiếp với khách hàng người nhật&nbsp;</li><li>Tham gia quản lý tiến độ dự án, báo cáo công việc định kỳ với khách hàng và quản lý dự án&nbsp;</li><li>Loại hình dự án: Các hệ thống web-application (Java, NodeJS, ReactJS, PHP, HTML, CSS,..)</li></ul>

BrSE Engineer

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<ul><li>Participate in the company’s project to develop the Japanese market.</li></ul>

Junior Python Developer

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<ul><li>Participate in and develop the unit’s projects</li><li>Training to become a bilingual BA for the Japanese market</li><li>Follow tasks assigned by the mentor or leader</li></ul>

Business Analyst (English & Japanese)

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We seek a skilled Business Analyst with 3-5 years of experience to join our team. The&nbsp; ideal candidate will bridge the gap between business stakeholders and technical teams&nbsp; and have some exposure to ESG concepts.&nbsp;Key Responsibilities&nbsp;<ul><li style="margin-left:18.875045776367188pt;">Analyze and document business requirements, create functional specifications,&nbsp; and user stories.</li><li style="margin-left:18.875045776367188pt;">Facilitate stakeholder workshops and requirement gathering sessions.</li><li style="margin-left:18.875045776367188pt;">Collaborate with cross-functional teams to identify process improvements.</li><li style="margin-left:18.875045776367188pt;">Participate in Scrum ceremonies and support Agile development processes.</li><li style="margin-left:18.875045776367188pt;">Conduct gap analyses and feasibility studies for proposed changes.</li><li style="margin-left:18.875045776367188pt;">Support testing activities, including UAT and system integration testing.</li><li style="margin-left:18.875045776367188pt;">Perform data analysis to provide business insights and recommendations.</li><li style="margin-left:18.875045776367188pt;">Assist with ESG-related initiatives and reporting as needed.&nbsp;</li></ul>

Business Analyst (English - DU33)

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<ul><li>Analyze project requirements and design specifications.</li><li>Develop and execute test plans, test cases, and test data.</li><li>Perform functional and non-functional testing for web and API applications.</li><li>Collaborate with related teams to ensure product quality.</li><li>Identify, track, and verify defects throughout the testing process.</li><li>Support process improvement and knowledge sharing within the QA team.</li><li>Mentor and guide a small QA team (2–3 members) when needed.</li></ul>

Senior Japanese Tester

senior-japanese-tester

<ul><li>Participate in outsourced projects for clients in the Japanese market.</li><li>Act as a bridge between Japanese clients and the offshore project team in Vietnam.</li><li>Work directly with Japanese clients: communicate, negotiate, receive, and analyze software project requirements, then convey them to the project team in Vietnam.</li><li>Plan, monitor progress, and manage the onsite team.</li><li>Perform other tasks as assigned.</li></ul>

AWS Engineer (onsite in Japan)

AWS Engineer (onsite in Japan)

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