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About This Role
Overview:
Every moment of every day, people all over the world turn to Panasonic to make their lives simpler, more enjoyable, more productive and more secure. Since our founding almost a century ago, we’ve been committed to improving peoples’ lives and making the world a better place–one customer, one business, one innovative leap at a time. Come join our journey!
Responsibilities: Sr. Business Analyst, AI & Automation
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Meet the Recruiter: Amber SmallwoodWhat You'll Get To Do: The Senior Business Analyst, AI & Automation identifies, evaluates, and advances business process improvement opportunities using robotic process automation, workflow automation, intelligent document processing, artificial intelligence, and related digital capabilities. The role leads business analysis activities across the automation lifecycle, including process discovery, feasibility assessment, requirements definition, business-case development, solution-design collaboration, User Acceptance Testing, change readiness, deployment support, and value realization. The Senior Business Analyst serves as a trusted advisor to business stakeholders and partners with technical, data, security, architecture, and governance teams to deliver secure, scalable, supportable, and measurable business outcomes.
Business Relationship
- Build and maintain consultative relationships with business unit executives and management.
- Serve as a subject matter expert on AI, automation, and process-improvement opportunities for assigned business areas.
- Facilitate discovery workshops to understand business challenges and opportunities for AI-assisted work.
- Educate stakeholders on available AI and automation capabilities and appropriate use cases.
- Support business readiness, process adoption, and change management activities.
Manage and Maximize Demand for PEXNA IT Services
- Manage demands from assigned business units, representing AI and Automation Service offerings.
- Identify opportunities to improve business processes through automation, AI, workflow optimization, or process redesign.
- Develop business requirements, business cases, and prioritization recommendations.
- Secure management and financial approvals.
- Escalate issues and take corrective action.
- Assess incoming AI and automation requests for business value, feasibility, expected outcomes, and alignment with organizational priorities.
- Document current-state and future-state processes to support evaluation of AI and automation opportunities.
- Develop business cases and value estimates for AI-enabled process improvements.
- Partner with technical teams to ensure business requirements are clearly defined and understood.
- Manage assigned automation opportunities through the full delivery lifecycle, including intake, process discovery, requirements definition, estimation, prioritization, design collaboration, User Acceptance Testing, change control, production readiness, deployment, hypercare, and benefits realization.
- Maintain visibility into assigned initiatives, dependencies, risks, decisions, approvals, delivery status, and business outcomes.
- Coordinate business and technical stakeholders to ensure that requirements remain traceable through design, testing, deployment, and operational transition.
AI, Automation & Business Transformation
- Participate in transformation initiatives.
- In conjunction with the business unit, develop AI and automation transformation roadmaps aligned to business objectives.
- Review project status reports and escalate issues as needed.
- Evaluate opportunities to improve employee productivity through AI-enabled solutions.
- Support implementation and adoption of AI, automation, and workflow optimization initiatives.
- Measure and report on business outcomes, process improvements, and value realization.
- Partner with data, security, and technology teams to ensure solutions comply with established standards and policies.
- Assess automation candidates for process stability, rules maturity, transaction volume, exception rates, data quality, system dependencies, security considerations, business continuity requirements, technical constraints, and expected value.
- Identify circumstances where process redesign, workflow automation, integration, RPA, intelligent document processing, conversational AI, Agentic AI, or a non-automation solution may be the most appropriate approach.
- Challenge requests constructively when automation would add unnecessary complexity, risk, or support cost.
- Support compliance with established AI governance, privacy, risk management, and security requirements.
Business Planning
- Participate in annual Business Planning to ensure alignment of IT AI and Automation services with future business needs.
- Coordinate periodic business review meetings. Ensure that any IT AI and Automation service deficiency is identified and corrected
- Approve the annual business unit service plan and budget
- Assist business units with identifying future AI and automation opportunities aligned to operational objectives.
- Participate in roadmap planning for AI and automation initiatives within assigned business areas.
- Provide input regarding emerging technology capabilities and potential business applications.
Scope: Budget, indirect employees indirect and other resources managed.
- Manage business relationships for internal Business Unit customers, understand customer process and needs in order define and deliver appropriate IT Automation services available from Panasonic, and coordinate resultant project activity.
- Understand and prepare business requirements, project plans, assess project resource needs and support resource planning.
- Ensure compliance with all IT policies and procedures.
Qualifications: What You'll Bring:
Required Education & Experience:
- Bachelor's degree in Business, Information Systems, Engineering, Data Analytics, or related field.
- 5+ years of progressive responsibility in Business Analysis, Process Improvement, Digital Transformation, or related experience.
- 4+ years supporting Robotic Process Automation, Intelligent Automation, Workflow Automation, or AI-enabled business solutions in an enterprise environment.
- Demonstrated experience with process discovery, current-state and future-state process modeling, root-cause analysis, and requirements elicitation.
- Demonstrated experience creating Process Definition Documents, business requirements, user stories, acceptance criteria, test scenarios, and User Acceptance Testing (UAT) documentation.
- Experience supporting automation solutions from intake through production deployment, transition to support, and post-implementation validation.
- Experience coordinating across business owners, developers, solution architects, cybersecurity, data, infrastructure, and third-party delivery partners.
- Experience developing business cases, establishing baselines, estimating benefits, defining success metrics, and measuring realized business outcomes.
- Knowledge of Agile methodologies, systems development lifecycle (SDLC), change control, testing, and production-readiness practices.
- Ability to understand integrations, application programming interfaces (APIs), structured data, authentication concepts, service accounts, queues, and exception-handling approaches.
Preferred Education & Experience:
- Experience supporting business adoption, change management, training, or operational readiness activities associated with new technologies.
- Experience with Intelligent Automation technologies such as workflow automation, robotic process automation (RPA), intelligent document processing (IDP), conversational AI, generative AI, or related capabilities.
- Familiarity with UiPath, Automation Hub, Insights, Orchestrator, Task Capture, Document Understanding, ServiceNow, Microsoft Power Automate, Copilot Studio, Microsoft 365 Copilot, SAP, or comparable enterprise platforms.
- Experience supporting Finance, Accounting, Procurement, Supply Chain, Human Resources, Information Technology, or other control-sensitive business processes.
- Experience working with distributed or third-party delivery teams.
- Familiarity with audit controls, data privacy, cybersecurity, records management, and segregation-of-duties principles.
- Basic proficiency with Excel and business data-analysis tools; SQL or Power BI experience preferred.
- One or more certifications in ITIL, Business Relationship Management (BRM), Project Management Professional (PMP), Certified Business Analysis Professional (CBAP), Six Sigma, or comparable disciplines.
Competencies:
- Demonstrates strong analytical and problem-solving skills with the ability to evaluate complex business processes and identify opportunities for improvement.
- Ability to identify opportunities where Artificial Intelligence (AI), automation, process redesign, or technology-enabled solutions can improve business performance, operational efficiency, compliance, or user experience.
- Demonstrates intellectual curiosity and continuous learning regarding emerging technologies, industry trends, AI capabilities, and business process innovation.
- Works independently with minimal supervision while effectively managing competing priorities and delivering high-quality results.
- Able to collaborate effectively with business stakeholders, project teams, technology teams, and external partners to achieve shared objectives.
- Demonstrates sound business judgment and balances innovation with operational, security, compliance, and risk management considerations.
- Demonstrates flexibility and adaptability in support of changing business priorities, technologies, and organizational needs.
Communications:
- Regular communications with all levels of customer/business unit and IT management regarding process improvement, automation, and AI-related initiatives.
- Facilitate discussions with business stakeholders to understand requirements, evaluate opportunities, and align on recommended solutions.
- Communicate process improvement, automation, and AI concepts in a manner appropriate for both technical and non-technical audiences.
- Present findings, recommendations, business cases, and project status updates to business and technology stakeholders.
- Influence stakeholders and build consensus regarding process improvements, automation opportunities, and AI-enabled business solutions.
- Collaborate with technology teams, project teams, vendors, and business partners to ensure successful delivery and adoption of solutions.
- Communicate business priorities, requirements, and operational impacts to technology delivery teams and service providers.
- Communicate sensitive issues, risks, dependencies, and recommendations in a constructive and solutions-oriented manner.
- Model Panasonic's Basic Business Principles in all interactions.
Other Requirements:
- Up to 5% Travel as required
Benefits & Perks - What's In It For You:
Panasonic prioritizes total well-being and offers comprehensive benefits options to support physical, emotional, financial, social, and environmental health:
- Health Benefits – Offering medical, dental, vision, prescription plans, plus Health Savings Account and Flexible Spending Account options.
- Voluntary Benefits – Life, accident, critical illness, disability, legal, identity theft, and pet insurance.
- Panasonic Retirement Savings & Investment Plan (PRSIP) – 401(k) plan with company matching contributions and immediate vesting.
- Paid Time-Off Benefits – Vacation, holidays, personal days, sick leave, volunteer, and parental & caregiver leave.
- Educational Assistance – Tuition reimbursement for job-related courses after six months of service.
- Health Management and Wellbeing Programs –Lifestyle Spending Account, EAP, virtual health management, chronic condition, neurodiversity, tobacco cessation, substance abuse support, and life stage and fertility resources. Available to eligible employees starting the first day of the month following your start date. Eligibility for each benefit may vary based on employment status, location, and length of service.
- Employee Recognition Program - High5 employee recognition and awards platform, quarterly and annual employee recognition
- Annual Bonus Program - Opportunity for an annual performance-based bonus.
We Take Opportunity Seriously:
At Panasonic, we are committed to a workplace that genuinely fosters inclusion and belonging. Fairness and Honesty have been part of our core values for more than 100 years and we are proud of our diverse culture as an equal opportunity employer.
The wage range of $100,000 - $115,000 is just one component of Panasonic’s total package. Actual compensation varies depending on the individual’s knowledge, skills, experience, and location. This role may be eligible for discretionary bonuses and incentives.
We understand that your career search may look different than others and embrace the professional, personal, educational, and volunteer opportunities through which people gain experience. If you are actively looking or starting to explore new opportunities, send us your application!
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristic protected by law. All qualified individuals are required to perform the essential functions of the job with or without reasonable accommodation.
*Due to the high volume of responses, we will only be able to respond to candidates of interest. All candidates must have valid authorization to work in the U.S.*Thank you for your interest in Panasonic. #LI-AS1 #LI-REMOTE
Salary Context
This $100K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Panasonic, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $100K to $115K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Panasonic AI Hiring
Panasonic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $115K - $115K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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