Sr. Data Scientist

Staffing Science; Phoenix (AZ); Hybrid; USD 160,000 - 210,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

Staffing Science in Phoenix, AZ is seeking a Senior Data Scientist who will architect ML solutions at scale, mentor teammates, and serve as the go-to expert for stakeholders seeking clear, data-driven answers.

You’ll design and deploy ML systems with real dollar impact, build pipelines with KubeFlow/Airflow/Dataflow, and translate complex models into plain business terms for leadership. Hybrid work arrangement.

Qualifications

• PhD or Master's in a quantitative field.
• Hands-on experience with TensorFlow or PyTorch, Python, SQL.
• Track record shipping models to production.
• Comfort owning ambiguity and mentoring others.
• Finance or economics background is a plus.

Responsibilities

• Design and deploy ML systems with real dollar impact.
• Build and maintain production pipelines across cloud environments.
• Explain model decisions to executives using interpretable techniques.
• Mentor junior data scientists and set technical standards.
• Partner with engineering and product leadership to scale ML usage.

Skills

TensorFlow

PyTorch

Python

SQL

Model deployment

Mentoring

Data storytelling

Education

Master's or PhD in Data Science, CS, Math, Engineering

Tools

KubeFlow

Airflow

Dataflow

Job description

U.S. Citizens only — sponsorship is not available now or in the future

Must be currently based in (or willing to relocate to) Arizona before your start date

Looking for a Senior Data Scientist who's done this before — built it, broke it, fixed it, and knows exactly why each decision mattered.

This isn't a "here's a dataset, go build a model" role. You'll be trusted to architect ML solutions at scale, mentor other data scientists, and be the person stakeholders come to when they need a straight answer wrapped in a good story.

What you'll actually do:

• Design and deploy ML systems (forecasting, gradient boosting, deep learning) with real dollar impact — not academic exercises
• Build and maintain production pipelines using KubeFlow, Airflow, or Dataflow across cloud environments
• Use SHAP and model interpretability techniques to defend your model's decisions to non-technical execs
• Mentor junior data scientists and set the technical bar for the team
• Partner directly with engineering and product leadership to shape how ML gets used company-wide

What makes you a fit:

• PhD or Master's in Data Science, CS, Math, Engineering, or related quantitative field
• Deep hands-on experience with TensorFlow or PyTorch, Python, SQL
• Track record shipping models to production — not just prototyping
• Comfortable owning ambiguity and mentoring others through it
• Bonus: background in Finance, Quantitative Finance, or Economics — this combination tends to produce people who are both sharp with numbers and genuinely persuasive communicators

Setup:

Hybrid — 1 day/week onsite in Phoenix AZ

If 'I built it, it broke, I fixed it, and here's what I learned' is a sentence you can say more than once — this is your role.