Senior Data Analyst - ML Systems & SageMaker

Energy Innovation Corp; Hobbs (NM); Hybrid; USD 87,000 - 121,000; Full time

Benefits offered by this job

Full benefits

Hybrid work model

Learning time for SageMaker

Job summary

Social Impact Partners seeks a Data Analyst to turn vague tech requests into high-growth systems, offering a senior title as you prove yourself. The role is hybrid in Hobbs, NM with a salary range of $87,000–$121,000 and benefits.

Expect to work with SageMaker, TensorFlow, and Hugging Face across a data-first engineering team. The ideal candidate will present to senior leadership, stay composed during scope changes, and continuously learn to improve test coverage and reliability.

Qualifications

• Comfort presenting to senior leadership.
• Calm when a Hobbs, NM client changes scope mid-stream.
• Curiosity that reads the docs before asking.
• A communicator who can disagree without making it personal.
• A point of view, held loosely and defended well.

Responsibilities

• Catch the NumPy race conditions that surface under Hobbs peak traffic.
• Resurrect flaky Hugging Face tests until the Hobbs, NM suite is trustworthy again.
• Watch Hugging Face error budgets and manage resources to avoid overruns.
• Champion engineering excellence and continuous learning within Social Impact Partners.
• Decode the undocumented TensorFlow service nobody remembers writing.
• Partner with QA to define test coverage and catch regressions early.

Skills

Leadership presentation

Composure under scope changes

Curiosity

Communication

Critical thinking

Tools

SageMaker

NumPy

Hugging Face

TensorFlow

Job description

Social Impact Partners seeks a Data Analyst to turn vague tech requests into high-growth systems, offering a senior title as you prove yourself. The role is hybrid in Hobbs, NM with a salary range of $87,000–$121,000 and benefits.

Expect to work with SageMaker, TensorFlow, and Hugging Face across a data-first engineering team. The ideal candidate will present to senior leadership, stay composed during scope changes, and continuously learn to improve test coverage and reliability.