Pricing Data Scientist: Forecasting & Deep Learning

The Home Depot; Atlanta (GA); Remote; USD 110,000 - 160,000; Full time

Benefits offered by this job

Health care benefits

401K

ESPP

Paid time off

Success sharing bonus

Job summary

The Home Depot is seeking a Data Scientist to lead price optimization through forecasted outcomes and promotions. You will drive profitability, efficiency, and customer experience by advancing analytics infrastructure and solving business problems using advanced methods.

You will present insights to non-technical audiences and collaborate with cross-functional teams. Strong Python/SQL skills and experience with predictive modelling and deep learning are preferred.

Qualifications

• Bachelor's degree in a quantitative field or equivalent.
• 3+ years of experience in business intelligence and analytics.
• PhD in a quantitative field is preferred but not required.

Responsibilities

• Design and develop algorithms and models to extract business insights from large datasets.
• Effectively communicate insights and recommendations to both technical and non-technical stakeholders; influence decision making.
• Collaborate with business partners and cross-functional teams to understand needs and drive impact.
• Explore new data science methods and build reusable data products and libraries for future projects.

Skills

Python

SQL

Forecasting

Deep Learning

Predictive modelling

Data mining

Education

Bachelor's degree in a quantitative field

PhD in a quantitative field preferred

Tools

TensorFlow

Scikit-learn

Job description

The Home Depot is seeking a Data Scientist to lead price optimization through forecasted outcomes and promotions. You will drive profitability, efficiency, and customer experience by advancing analytics infrastructure and solving business problems using advanced methods.

You will present insights to non-technical audiences and collaborate with cross-functional teams. Strong Python/SQL skills and experience with predictive modelling and deep learning are preferred.