Humane And Secure Environment Jobs in New York City, NY

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Treasury Manager

Company: ADAPT Community Network

Location: New York, NY

Posted Apr 07, 2025

Maintains batch control logs for cash deposits through assignment of batch sequences and G/L account offsets to cash. Bachelor’s degree in accounting required.

Frequently Asked Questions

What are typical salary ranges for Humane & Secure Environment roles by seniority level?
Junior (0‑2 yrs): $80k–$100k; Mid‑level (3‑5 yrs): $110k–$140k; Senior (5‑8 yrs): $150k–$190k; Lead/Principal (8+ yrs): $200k–$240k. These figures reflect average U.S. market rates for roles such as Ethical AI Engineer or Security Architect.
What skills and certifications are most valuable for these positions?
Key technical skills: Python, Java, Rust, TensorFlow/PyTorch for model development; OWASP ZAP, Snyk, Fortify for secure coding; Kubernetes and Istio for container security; GitOps and IaC (Terraform, Pulumi). Certifications: CISSP, CISM, ISO 27001 Lead Implementer, Certified Ethical Hacker (CEH), AI Governance Certificate (e.g., from IEEE or The Center for AI Ethics), and data‑privacy credentials such as CIPP/E or IAPP.
Are Humane & Secure Environment jobs available for remote work?
Most companies offer full‑remote or hybrid arrangements, especially for roles like Ethical AI Engineer and Privacy Engineer. However, some security‑audit or compliance roles may require occasional on‑site visits for penetration tests or regulatory inspections.
What career progression paths exist within this field?
A common trajectory starts as a Secure Software Developer → Ethical AI Engineer → Security Architect → AI Governance Lead → Chief Privacy Officer or Chief Information Security Officer. Progression involves gaining deeper policy expertise, leading cross‑functional projects, and publishing research on bias mitigation or zero‑trust design.
What are the emerging industry trends shaping Humane & Secure Environment careers?
Trends include AI governance frameworks (e.g., EU AI Act), privacy‑preserving ML techniques such as federated learning and differential privacy, secure multi‑party computation for collaborative AI, zero‑trust architectures in cloud environments, and increased regulatory scrutiny of algorithmic decision‑making.

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