About Medical Guardian:
Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we’re redefining what it means to age confidently and independently.
We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
Position Summary:
We are seeking a highly skilled and strategic Senior Security Engineer to strengthen and mature our enterprise security capabilities as part of the broader IT Operations organization. This role is responsible for securing cloud infrastructure, AI platforms, applications, endpoints, identity systems, and core operational environments.
The Senior Security Engineer will work closely with Infrastructure, Engineering, Compliance, QA, IT Operations, and external security service providers to proactively reduce risk while enabling business growth. This position reports directly to the Head of Infrastructure and Information Security, with a dotted-line reporting relationship to the CISO for strategic alignment, governance oversight, and security program direction.
This role serves as the primary technical lead for security incident response, responsible for coordinating internal response teams, activating third-party incident response partners when required, and leading containment and recovery efforts during active security events. The position requires the ability to respond to and manage security incidents outside of standard business hours when necessary.
This is a hands-on senior engineering role with architectural ownership, external partner oversight, and mentorship responsibilities.
Key Responsibilities:
Security Architecture and Engineering
Design and implement secure architecture patterns across Azure and AWS cloud environments, as well as on-prem and hybrid infrastructures Lead security design reviews for infrastructure and application initiatives Engineer and optimize enterprise security controls across endpoint protection, threat detection and response, network security, email security, data protection, cloud access governance, and privileged access management Define and implement Zero Trust principlesCloud Security, Azure Focused
Harden Azure security posture including Entra ID governance, RBAC design, Conditional Access, PIM, Defender for Cloud, Defender for Cloud Apps, and Private Link architecture Implement and manage cloud posture management and cloud workload protection capabilities, including CSPM and CNAPP tooling Secure Kubernetes and containerized workloads Automate security guardrails using infrastructure as code such as Terraform, Bicep, and CloudFormation Implement enterprise data classification, DLP, encryption, and tenant-level controls across Microsoft 365 and Azure to prevent data exfiltration and unauthorized AI service accessAI and Emerging Technology Security
Design, implement, and enforce security controls for enterprise AI platforms including Azure OpenAI, Microsoft Copilot, Azure Machine Learning, and related AI services Secure AI model training data, inference endpoints, APIs, and service principals while enforcing governance controls to prevent exposure of sensitive or regulated data Develop guardrails to detect and prevent shadow AI adoption Evaluate third-party AI tools for security, privacy, and data residency risks Partner with Legal and Compliance teams to support responsible AI governance and regulatory requirementsApplication Security
Partner with DevOps and Engineering teams to integrate automated application security testing, including static analysis, dynamic testing, and secret detection, into CI and CD pipelines prior to deployment Perform threat modeling and architecture risk assessmentsThreat Detection and Incident Response
Serve as incident response lead for security events, coordinating internal response teams and activating third-party incident response partners as needed Lead containment, eradication, and recovery efforts during security incidents Enhance detection engineering use cases within SIEM and develop automated response playbooks Lead post-incident reviews and root cause analysis Lead and facilitate regular incident response tabletop exercises and coordinated response simulations to validate detection, escalation, and cross-functional readinessVulnerability Management
Oversee enterprise vulnerability management including scanning, risk-based prioritization, and remediation tracking Develop metrics and reporting for executive visibilityCompliance and Risk
Support regulatory requirements including HIPAA, HITRUST, SOC 2, and PCI-DSS as applicable Assist with audits and evidence collection Develop and maintain security policies and standards Perform third-party risk assessmentsSecurity Operations and External Partner Management
Oversee MDR detection coverage, alert tuning, escalation workflows, service level adherence, and integration of logging and telemetry between internal systems and third-party providers Collaborate with the MSP on infrastructure security hardening, patching strategy, endpoint protection, and configuration management Drive continuous improvement through regular performance reviews and security posture assessments with external partnersLeadership and Mentorship
Provide technical guidance and drive security best practices across IT and Engineering initiatives Serve as escalation point for complex security issuesRequirements
Required Qualifications
Must be legally authorized to work in the United States without the need for employer sponsorship now or in the future 5 or more years of progressive experience in cybersecurity engineering Strong experience in Azure security architecture and hands-on implementation of controls including Entra ID, Conditional Access, PIM, Defender for Cloud, and Private Endpoints Deep understanding of network security, identity and access architecture, endpoint protection, and security monitoring and detection engineering principles Experience securing AI and ML platforms or cloud-native AI services Experience implementing enterprise data protection controls including DLP, Purview, labeling, encryption, and key management Experience with infrastructure as code and automation using Python, PowerShell, Terraform, Bicep, or similar tools Experience securing CI and CD pipelines and containerized environments Strong knowledge of security frameworks including NIST, CIS, and ISO 27001 Experience managing third-party security operations relationships and holding vendors accountable to defined service levelsPreferred Qualifications
Experience in regulated industries such as healthcare Experience implementing Zero Trust architectures Security certifications such as CISSP or CCSP strongly preferred. Azure security certifications including AZ-500 highly valued. GIAC certifications such as GCED or GCIA and OSCP are considered a plus.Work Environment & Requirements:
Hybrid work model with on-site presence required two days per week at the Philadelphia location Serve as the primary incident response lead, including availability outside standard business hours to coordinate and manage security incidents and engage third-party incident response partners when necessary Candidates must be authorized to work in the United States without current or future need for visa sponsorship.Benefits
Health Care Plan (Medical, Dental & Vision)Paid Time Off (Vacation, Sick Time Off & Holidays)Company Paid Short Term Disability and Life InsuranceRetirement Plan (401k) with Company Match Read LessInventory Specialist
Every day, Medical Guardian fulfills the same mission we started with in 2005: To help others live a life without limits. We do this by being a leading provider of innovative senior health services.
From valued customers to dedicated employees, we treat everyone with the same respect and kindness. As Medical Guardian grows, we are looking for the best and brightest to join our mission-driven organization within an extraordinary company culture. We are currently seeking an Inventory Specialist to join us.
Job Location:
Sharon Hill, PA (Delaware County)
Job Type:
Full-Time/Regular
Level of Education:
High School Diploma/Equivalent
Pay:
$17 - $21/hour
What is the shift for the position?
Weekdays: Monday to Friday (8:30am to 5pm)
Responsibilities:
Monitor and maintain accurate inventory levels to support business and operational requirements while optimizing inventory turns and minimizing excess or obsolete stock.Conduct regular cycle counts and physical inventory audits using established methodologies (e.g., ABC counting), investigate discrepancies, and implement corrective actions to maintain high inventory accuracy.Assist with ordering, receiving, inspecting, and stocking materials and supplies; verify shipments for accuracy, quality, and proper documentation.Maintain accurate and timely inventory records in the Warehouse Management System (WMS), ensuring all inventory movements, adjustments, and transactions are properly documented.Generate and analyze inventory reports and KPIs to identify trends, risks, shortages, or overstock conditions, and communicate findings to relevant stakeholders.Collaborate closely with Fulfillment, Purchasing, and Operations teams to forecast inventory needs based on demand trends, order volume, and business activity.Identify inefficiencies in inventory and warehouse processes and actively participate in continuous improvement initiatives to enhance accuracy, workflow, space utilization, and labor efficiency.Ensure all inventory management activities comply with company policies, quality standards, and applicable regulatory requirements.Work overtime or weekends as needed to support inventory and fulfillment requirements.Operate warehouse equipment, including order pickers, in a safe and compliant manner.Perform other duties as assigned to support warehouse and inventory operations.Requirements
Requirements:
2+ years of experience in inventory control, warehouse operations, or supply chain management.Strong attention to detail with a proven ability to maintain high levels of accuracy.Ability to analyze inventory data, identify trends, and provide actionable insights.Working knowledge of standard warehouse operations, including receiving, storing, picking, and shipping.Proficiency in inventory management systems (e.g., Datex WMS) and Microsoft Office Suite, particularly Excel.Excellent time management and organizational skills with the ability to prioritize multiple tasks in a fast-paced environment.Strong verbal and written communication skills and the ability to collaborate effectively across departments.Ability to lift up to 50 lbs., stand or walk for extended periods, perform repetitive motions, and safely climb ladders to access and place inventory on upper storage levels.Must be legally authorized to work in the United States without the need for employer sponsorship now or in the future. Ability to work in a warehouse environment with exposure to varying temperatures, noise levels, and warehouse equipment.Preferred Qualifications:
Experience with inventory optimization concepts such as safety stock, reorder points, FIFO/LIFO, and inventory turns.Previous experience operating an order picker or similar material handling equipment.Exposure to continuous improvement or Lean warehouse practices.Benefits
Health Care Plan (Medical, Dental & Vision)Paid Time Off (Vacation & Public Holidays)Short Term & Long Term DisabilityRetirement Plan (401k) Read LessCompany Background:
Founded in 2005, Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we’re redefining what it means to age confidently and independently.
We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
Medical Guardian boasts a 95% customer satisfaction rate, a #1 ranking on 16 medical alert consumer choice sites and achieves a 4.7+ star rating on Google Reviews.
Position Overview:
Medical Guardian is scaling its financial planning & analysis team in preparation for accelerated growth. The Senior Financial Analyst position is a newly created role that will report to the to-be-hired Senior Finance Manager / Finance Manager. This is a highly visible and critical cross-functional role driving operational execution across Medical Guardian.
Successful candidates thrive in fast-paced, dynamic environments and are comfortable with change. They have a strong business acumen and the ability to apply it to complex scenarios. They can concisely and precisely provide and / or present financial information to cross-functional leaders. They have strong intellectual curiosity, always asking why and not afraid to question the status quo. They have a high level of attention-to-detail and strong written and verbal communication skills. They model resilience when faced with something new, complex or challenging. They can influence key financial and funding decisions.
Responsibilities:
Support the preparation of monthly operational and KPI reporting (total company, business unit, and customer segment levels) Support the preparation of monthly financial reporting package (3-statement model, monthly business review deck, monthly financial template for PE-ownership) Effectively collaborate with the accounting team on month-end general ledger close, including analysis of actual results vs budget or forecast Support the preparation of annual budgets and quarterly re-forecasts Support the preparation of rolling cash flow forecasts, including reconciling forecasted cash flows to forecasted financial results Support the preparation and refresh of long-term strategic financial plans as requested Create financial models and business cases to support strategic decision-making as requested Develop volume and rate analysis to enhance understanding of variance drivers, variable vs fixed cost structures, and margin rates Partner with the technology team to enhance automated reporting capabilities Prepare ad hoc analysis as requestedEssential Qualifications:
Education
Bachelor’s Degree in Accounting, Finance, or Business Administration required MBA is preferred CPA or CFA is preferredWork Experience
A minimum of 4 years of professional experience in progressive financial planning & analysis roles is required Experience in high-growth private equity-owned business is preferred Experience in direct-to-consumer recurring revenue business models is preferredSkills
Advanced Excel skills for financial modeling is required Advanced PowerPoint skills for reporting, data visualization, and presentations is required Understanding of accounting principles, financial statements, financial analysis and financial modeling is requiredBenefits
Health Care Plan (Medical, Dental & Vision)Paid Time Off (Vacation, Sick Time Off & Holidays)Company Paid Short Term Disability and Life InsuranceRetirement Plan (401k) with Company Match Read Less About Medical Guardian:
Founded in 2005, Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we’re redefining what it means to age confidently and independently.
We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
Medical Guardian boasts a 95% customer satisfaction rate, a #1 ranking on 16 medical alert consumer choice sites and achieves a 4.7+ star rating on Google Reviews.
Position Overview:
We are looking for a Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.
This is a hands-on model-building role first. The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement. They should also be able to operate with the maturity of a principal-level engineer: shaping unclear problems, making pragmatic technical decisions, mentoring others, and driving work forward without waiting for perfect requirements.
Key Responsibilities:
Hands-On Model Development
Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support. Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation. Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness. Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features. Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries. Move quickly from data exploration to prototype to validated model to production-ready capability.Scoring, Scorecards, and Transparent Models
Design and implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic. Build transparent and interpretable models where explainability is important, including logistic regression, generalized linear models, decision trees, monotonic models, calibrated models, scorecard-style models, or explainable boosting approaches. Evaluate models for accuracy, calibration, stability, drift, fairness, interpretability, and operational usefulness. Help stakeholders understand what a score represents, how it should be used, how it should not be used, and how changes in the score should be interpreted. Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand. Define the evidence needed to show that a model or score is valid, stable, explainable, actionable, and useful.Production ML and MLOps
Partner with data engineering, analytics engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows. Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking. Help define data pipelines, feature pipelines, inference flows, model outputs, feedback loops, and monitoring requirements. Ensure models are observable, supportable, secure, scalable, and aligned with enterprise architecture and governance expectations. Establish practical monitoring and feedback loops to determine whether models continue to perform and create value over time.Product and Rapid-Build Execution
Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making are important. Turn early-stage ideas, ambiguous business needs, and rough concepts into working ML products, scores, prototypes, and production capabilities. Bring a product-engineering mindset to ML development, including user needs, workflow integration, adoption, usability, feedback loops, and measurable outcomes. Drive work forward without waiting for perfect requirements, while still identifying critical assumptions, risks, dependencies, and evidence needed before scaling. Partner with business and product stakeholders to define MVPs, iterate quickly, learn from usage, and improve models over time. Make smart tradeoffs between quick prototypes, durable platforms, transparent models, GenAI-enabled workflows, and longer-term ML architecture.Generative AI and AI Automation
Support the design and development of GenAI-enabled solutions, including LLM-powered workflows, RAG, summarization, conversational agents, document intelligence, and decision-support tools. Help evaluate when GenAI is appropriate versus when traditional ML, rules, analytics, or transparent scoring models are a better fit. Partner with product, engineering, and business stakeholders to integrate predictive models, scores, and GenAI outputs into practical workflows. Apply appropriate evaluation, guardrails, monitoring, privacy controls, and human-in-the-loop processes for GenAI use cases. Help the organization balance innovation with explainability, safety, reliability, privacy, and operational usefulness.Requirement Shaping and Stakeholder Partnership
Work directly with business, product, analytics, operations, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger. Translate business questions into measurable ML objectives, target variables, features, validation approaches, and success metrics. Ask practical questions early: who will use the score, what action will it inform, what does a false positive or false negative mean, and how will we know the model is creating value? Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences. Help the team avoid becoming an AI ticket factory by shaping solutions, not just executing requests.Principal-Level Technical Leadership
Provide technical leadership through hands-on example, strong engineering judgment, and clear recommendations. Proactively identify model risks, data gaps, unclear requirements, design issues, and opportunities for improvement. Help establish practical standards for model development, validation, documentation, monitoring, and production readiness. Mentor other engineers and data scientists through code reviews, design reviews, modeling guidance, and shared best practices. Demonstrate high ownership by driving clarity, execution, and continuous improvement.Required Qualifications:
8+ years of professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields. 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration. 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments. Strong hands-on experience with Python and SQL. Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies. Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management. Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes. Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders. Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability. Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.Preferred Qualifications:
10+ years of relevant professional experience in ML, data science, applied AI, software engineering, decisioning systems, commercial software, or production analytics. Experience building scorecards, risk scores, health scores, engagement scores, churn scores, fraud scores, credit-style models, prioritization models, or operational decision-support models. Experience with transparent or interpretable models such as logistic regression, GLMs, GAMs, decision trees, monotonic models, calibrated models, scorecard-based models, or Explainable Boosting Machines. Experience designing score bands, thresholds, risk tiers, intervention rules, recommended actions, or decision logic based on model outputs. Experience working in commercial software, SaaS, digital products, gaming, fintech, healthtech, consumer technology, marketplace, or other product-driven environments. Experience building ML, AI, analytics, or decisioning capabilities embedded into customer-facing products, operational workflows, commercial platforms, or revenue-impacting systems. Experience in startup, scale-up, innovation lab, new product development, or rapid-build environments where the candidate had to operate with ambiguity and drive work forward independently. Experience partnering with product managers, designers, software engineers, business leaders, and operational teams to turn ML models into usable product capabilities. Experience building MVPs, validating assumptions, iterating based on feedback, and maturing prototypes into production systems. Experience with GenAI, LLMs, RAG, AI agents, prompt engineering, model evaluation, conversational AI, summarization, document intelligence, or AI-enabled workflow automation. Experience combining traditional ML models with GenAI-enabled workflows, such as using predictive scores to trigger outreach, summarize customer/member context, recommend next actions, or support human decision-making. Experience in healthcare, population health, remote patient monitoring, insurance, financial services, safety, operations, or other domains where model trust and explainability are important. Experience with MLOps practices including model registries, deployment pipelines, monitoring, drift detection, retraining strategies, and model governance.Success in This Role Looks Like:
High-quality models and scores are built, validated, deployed, monitored, and improved over time. Model outputs are explainable and trusted by business and operational stakeholders. Scores are connected to real decisions, workflows, interventions, or measurable outcomes. The organization moves faster because this person can turn ambiguity into working ML capabilities. The ML team has stronger standards for model development, validation, documentation, monitoring, and production readiness. Business partners understand what the models do, how to use them, where their limitations are, and how to interpret changes in outputs. The team avoids building models in isolation and instead builds ML capabilities that are connected to products, workflows, users, and business value. GenAI is applied thoughtfully where it improves workflow, decision support, summarization, automation, or user experience, without replacing appropriate model governance or human judgment.Benefits
Health Care Plan (Medical, Dental & Vision)Paid Time Off (Vacation, Sick Time Off & Holidays)Company Paid Short Term Disability and Life InsuranceRetirement Plan (401k) with Company Match Read Less