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Data Scientist

Charlotte, North Carolina, United States of America

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Apply at Lowe's

What you’ll work on

Full posting
  • Build models that influence forecasting, pricing, promotions, and supply chain decisions.

  • Develop and improve predictive models, forecasting engines, and machine learning capabilities supporting the Lowe’s Forecasting Platform and key merchandising retail strategies.

  • Work with large-scale retail data.

From the employer’s posting
What You’ll Do Build models that influence forecasting, pricing, promotions, and supply chain decisions. Develop and improve predictive models, forecasting engines, and machine learning capabilities supporting the Lowe’s Forecasting Platform and key merchandising retail strategies. Turn complex pricing questions into measurable insights. Apply statistical modeling, causal inference, and experimentation to demand modeling, price recommendations, measurement, halo/cannibalization, and test-and-learn initiatives.
Turn complex pricing questions into measurable insights. Apply statistical modeling, causal inference, and experimentation to demand modeling, price recommendations, measurement, halo/cannibalization, and test-and-learn initiatives. Work with large-scale retail data. Use SQL, Python, Spark/PySpark, and cloud platforms to prepare reusable datasets, validation checks, and model-ready features. Translate business challenges into data science solutions. Partner with Product, Engineering, Analytics, and Business teams to define model requirements, success metrics, and production-ready approaches.

What you’ll bring

All qualifications

Core experience

  • Bachelor’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field, or equivalent years of experience in lieu of education requirement, if applicable.
  • 2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting.
  • 1 year of SQL and programming experience.

Preferred experience

  • 2 years of experience with forecasting, demand planning, pricing, promotions, merchandising, retail, or supply chain analytics.
  • 2 years of experience with statistical modeling, causal inference, experimentation, predictive analytics, or prescriptive analytics in a large-scale enterprise environment.
  • 2 years of experience using SQL, Python, Spark/PySpark, cloud platforms, or ML workflow tools such as GCP, Vertex AI, MLflow, AWS, or Azure.
  • Experience partnering with cross-functional teams and translating model outputs into business recommendations, documentation, validation artifacts, or production requirements.
Qualification wording
Bachelor’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field, or equivalent years of experience in lieu of education requirement, if applicable.
2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting. Fewer years may be accepted with a master’s or doctorate degree.
1 year of SQL and programming experience. Fewer years may be accepted with a master’s or doctorate degree.
2 years of experience with forecasting, demand planning, pricing, promotions, merchandising, retail, or supply chain analytics.
2 years of experience with statistical modeling, causal inference, experimentation, predictive analytics, or prescriptive analytics in a large-scale enterprise environment.
2 years of experience using SQL, Python, Spark/PySpark, cloud platforms, or ML workflow tools such as GCP, Vertex AI, MLflow, AWS, or Azure.
Experience partnering with cross-functional teams and translating model outputs into business recommendations, documentation, validation artifacts, or production requirements.
Education & alternatives
- Bachelor’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field, or equivalent years of experience in lieu of education requirement, if applicable. - 2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting. Fewer years may be accepted with a master’s or doctorate degree. - 1 year of SQL and programming experience. Fewer years may be accepted with a master’s or doctorate degree.
- 2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting. Fewer years may be accepted with a master’s or doctorate degree. - 1 year of SQL and programming experience. Fewer years may be accepted with a master’s or doctorate degree. Preferred Qualifications

Tools in this posting

  • Python
  • SQL
  • AWS
  • Google Cloud (GCP)
  • MLflow
  • Spark
  • Azure
  • PySpark
Source — Tool mentions in context
- Turn complex pricing questions into measurable insights. Apply statistical modeling, causal inference, and experimentation to demand modeling, price recommendations, measurement, halo/cannibalization, and test-and-learn initiatives. - Work with large-scale retail data. Use SQL, Python, Spark/PySpark, and cloud platforms to prepare reusable datasets, validation checks, and model-ready features. - Translate business challenges into data science solutions. Partner with Product, Engineering, Analytics, and Business teams to define model requirements, success metrics, and production-ready approaches.
- 2 years of experience with statistical modeling, causal inference, experimentation, predictive analytics, or prescriptive analytics in a large-scale enterprise environment. - 2 years of experience using SQL, Python, Spark/PySpark, cloud platforms, or ML workflow tools such as GCP, Vertex AI, MLflow, AWS, or Azure. - Experience partnering with cross-functional teams and translating model outputs into business recommendations, documentation, validation artifacts, or production requirements.
- 2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting. Fewer years may be accepted with a master’s or doctorate degree. - 1 year of SQL and programming experience. Fewer years may be accepted with a master’s or doctorate degree. Preferred Qualifications

About Lowe's

(NYSE: LOW) is a FORTUNE® 100 home improvement company with total fiscal year 2025 sales of more than $86 billion.

In the employer’s words · Read in context

Job description

View original posting ↗

Innovate in Charlotte

Thank you for dedicating your time and talent to Lowe’s.  We want to give you more opportunities to learn and grow, so if you find a position you’re interested in below, we encourage you to apply!

Your Impact

Join Lowe’s Data, Analytics & Customer Insights organization and apply data science to business problems that directly influence how Lowe’s forecasts demand, plans inventory, and makes pricing and promotion decisions.

In this role, you’ll support data science initiatives across Merchandising and Supply Chain Intelligence, including Pricing & Promotion Analytics and the Lowe’s Forecasting Platform. You’ll partner with Product, Engineering, Analytics, and Business teams to build, validate, and translate models and insights that support demand forecasting, pricing decisions, measurement, inventory planning, and merchant-facing technology products.

This is an opportunity to work at the intersection of data science, retail decision-making, and technology, turning complex data into scalable solutions and actionable recommendations.

What You’ll Do

  • Build models that influence forecasting, pricing, promotions, and supply chain decisions. Develop and improve predictive models, forecasting engines, and machine learning capabilities supporting the Lowe’s Forecasting Platform and key merchandising retail strategies.

  • Turn complex pricing questions into measurable insights. Apply statistical modeling, causal inference, and experimentation to demand modeling, price recommendations, measurement, halo/cannibalization, and test-and-learn initiatives.

  • Work with large-scale retail data. Use SQL, Python, Spark/PySpark, and cloud platforms to prepare reusable datasets, validation checks, and model-ready features.

  • Translate business challenges into data science solutions. Partner with Product, Engineering, Analytics, and Business teams to define model requirements, success metrics, and production-ready approaches.

  • Help ensure models deliver measurable business value. Evaluate model performance, forecast variance, assumptions, limitations, explainability, and adoption risks.

  • Make complex findings actionable. Communicate recommendations to technical and non-technical audiences through dashboards, validation summaries, documentation, and decision-support materials.

Required Qualifications

  • Bachelor’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field, or equivalent years of experience in lieu of education requirement, if applicable.

  • 2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting. Fewer years may be accepted with a master’s or doctorate degree.

  • 1 year of SQL and programming experience. Fewer years may be accepted with a master’s or doctorate degree.

Preferred Qualifications

We encourage you to apply even if you do not have all the preferred skills or experience.

  • 2 years of experience with forecasting, demand planning, pricing, promotions, merchandising, retail, or supply chain analytics.

  • 2 years of experience with statistical modeling, causal inference, experimentation, predictive analytics, or prescriptive analytics in a large-scale enterprise environment.

  • 2 years of experience using SQL, Python, Spark/PySpark, cloud platforms, or ML workflow tools such as GCP, Vertex AI, MLflow, AWS, or Azure.

  • Experience partnering with cross-functional teams and translating model outputs into business recommendations, documentation, validation artifacts, or production requirements.


About Lowe’s

Lowe’s Companies, Inc. (NYSE: LOW) is a FORTUNE® 100 home improvement company with total fiscal year 2025 sales of more than $86 billion. Lowe’s employs approximately 300,000 associates and operates over 1,750 home improvement stores, 540 branches and 120 distribution centers. Lowe’s is a core value S&P 500 equity stock and a dividend aristocrat. Based in Mooresville, N.C., Lowe’s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com.  

Lowe’s is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, ancestry, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under federal, state, or local law.

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Location & working pattern

Charlotte, North Carolina, United States of America

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First seen by us
Sep 3, 2026
Recorded sightings
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Last seen by us
Oct 9, 2026

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