Walmart Entry Level Jobs @ Get Dream Job

Walmart; Montgomery (AL); On-site; USD 27,552 - 41,328; Full time

Job summary

Walmart in Montgomery, USA is seeking a Principal Data Scientist to drive advanced analytics and ML initiatives. The role focuses on turning complex business problems into data-driven solutions, leveraging neural networks and causal inference to deliver measurable value.

You will collaborate with cross-functional teams to prototype models, deploy algorithms, and monitor performance while mentoring junior data scientists and communicating insights to executives.

Qualifications

• Experience with data exploration, visualization, and deriving insights.
• Hands-on through model deployment and neural networks.
• Proficiency in SQL and Python/R.
• Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch).
• Causal inference modeling experience.
• Experience with core ETL processes; SparkSQL or PySpark a plus.
• Experience working in a cloud environment (Azure, Google Cloud).
• Ability to mentor junior data scientists.

Responsibilities

• Lead business-process disclosure and planning with cross-functional teams.
• Transform business problems into ML or AI challenges and propose solutions.
• Prototype ML models with Retail Science to influence decision drivers.
• Lead on-demand initiatives to improve decisions and model trade-offs.
• Collaborate with Experimentation to plan analyses and metrics.
• Provide on-call analysis and support for hyper-parameter tuning.
• Communicate outcomes to business stakeholders and support model advancement.

Skills

Data exploration

Visualization

Insights

Model deployment

Neural networks

SQL

Python

R

Causal inference

Storytelling

Mentoring

Education

Bachelor's degree

Tools

Scikit-learn

TensorFlow

PyTorch

SparkSQL

PySpark

Azure

Google Cloud

ETL pipelines

Job description

Job Overview

Principal Data Scientist – Salary $20–30/hour – Company: Walmart – Location: Montgomery, USA – Educational Requirements: Bachelor’s Degree.

What You'll Do

• Collaborate with the Science, Executive, and System teams to lead current status business‑process disclosure and planning.
• Re‑think business problems into machine‑learning or computer‑based intelligence challenges and propose algorithmic solutions to achieve partner, client, and financial goals.
• Team up with Retail Science to rapidly prototype ML models for revising key decision drivers and cycles within Walmart US.
• Lead on‑request initiatives to improve decisions and model trade‑offs involving ML/machine‑learning calculations.
• Collaborate with the Experimentation team on planning analyses and estimating progress metrics.
• Provide on‑call analysis and scientific support for hyper‑parameter tuning and optimal algorithmic weighting of trade‑offs.
• Support the advancement of various decision models (e.g., client and partner models) by working with Retail Science groups and communicating outcomes to business stakeholders.

What You'll Bring

• Inquisitive attitude and enthusiasm for exploring large, complex datasets.
• Experience with data exploration, visualization, insights, model deployment, and neural networks.
• Familiarity with ML frameworks such as Scikit‑learn, TensorFlow, and PyTorch.
• Experience building causal inference models and related systems.
• Strong SQL and Python/R coding skills.
• Experience with core ETL processes; knowledge of SparkSQL or PySpark is a plus.
• Experience working in a cloud environment (Azure, Google Cloud, etc.).
• Model monitoring practices.
• Mentoring or leading junior data scientists.
• Ability to translate business problems into data‑science solutions and deliver measurable business value.
• Strong storytelling skills – able to convey complex concepts through clear narratives and visuals.

You'll Inspire Us If…

• You are a curious, out‑of‑the‑box thinker who continually seeks opportunities to improve business processes with ML and AI initiatives.
• You have a proven track record of building causal inference models.
• You can independently tackle complex problems from problem definition to data‑science solution, model experimentation, and deployment.