AI Data Scientist: End-to-End ML & Solutions

Invisible, Inc.; New York (NY); Hybrid; USD 122,000 - 199,000; Full time

Job summary

Invisible Technologies is seeking a Data Scientist to build and deploy AI-powered solutions for clients, owning projects end-to-end—from scoping to deployment—while partnering with delivery teams and non-technical stakeholders.

In this hybrid role, you’ll blend hands-on data science with advisory work, translating real-world constraints into scalable models and reusable tooling that accelerates future work.

Qualifications

• 1+ years of hands-on experience in data science through roles, internships, or projects
• A Bachelor's degree in Data Science, Computer Science, or related field
• Strong Python skills and familiarity with NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch
• Solid understanding of statistical modeling end-to-end
• Strong communication skills and ability to explain data decisions to non-technical stakeholders
• Entrepreneurial mindset and comfort with fast-moving, unstructured environments
• Willingness to be on-call for customers when situations arise

Responsibilities

• Partner with delivery leaders to scope, develop, and deploy end-to-end statistical and ML solutions to operational problems
• Dig into customer problems to identify optimizations, then turn them into scalable solutions
• Translate messy real-world constraints (e.g., inconsistent data, latency requirements) into elegant solutions
• Iterate quickly based on real-time feedback from operators and clients
• Build reusable tooling and infrastructure that accelerates future model development and deployment

Skills

Python

Statistical Modeling

ML & AI

Communication Skills

Entrepreneurial Mindset

On-call Readiness

Education

Bachelor's degree in Data Science / CS

Tools

NumPy

Pandas

scikit-learn

TensorFlow

PyTorch

Job description

Invisible Technologies is seeking a Data Scientist to build and deploy AI-powered solutions for clients, owning projects end-to-end—from scoping to deployment—while partnering with delivery teams and non-technical stakeholders.

In this hybrid role, you’ll blend hands-on data science with advisory work, translating real-world constraints into scalable models and reusable tooling that accelerates future work.