Data Scientist

US LBM

full-time

Posted on: 9/26/2025

Location: 🇺🇸 United States

✨ AI Apply

Mid-LevelSenior

KerasNumpyPandasPythonPyTorchScikit-LearnSQLTableauTensorflow

About the role

Drive US LBM initiatives and provide meaningful insights to deliver business value across analytics, pricing, purchasing, and supply chain.
Cultivate a culture of data-driven decision-making across the business and collaborate with cross-functional teams (operations, finance, sales, supply chain).
Partner with business stakeholders to gather requirements and translate them into technical specifications and process documentation.
Conduct exploratory data analysis, generate summary statistics and visualizations, and identify patterns, anomalies, and relationships.
Apply statistical techniques (regression analysis, hypothesis testing) to extract actionable insights and validate models.
Develop, train, and evaluate machine learning models for classification, regression, clustering, forecasting, and recommendation tasks.
Build predictive models for demand forecasting, customer behavior prediction, and product recommendation, and perform hyperparameter tuning and cross-validation.
Develop, fine-tune, deploy, and integrate Large Language Models for text generation, sentiment analysis, and information retrieval into data pipelines.
Develop custom algorithms and tools, continuously improve models based on new data and feedback, and experiment with new techniques and technologies.

Bachelor's Degree or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
5+ years of experience working in Data science roles operational environments or business consulting organizations.
Working knowledge of large data set manipulation using SQL.
Hands-on experience with state-of-the-art LLMs such as GPT-3, GPT-4, BERT, T5, etc.
Proficiency in programming languages commonly used in machine learning such as Python.
Comfortable with libraries like TensorFlow, PyTorch, scikit-learn, or Keras.
Understanding of statistical concepts and techniques for data preprocessing, model evaluation, and interpretation of results.
Ability to manipulate and preprocess data efficiently using libraries like pandas and NumPy.
Familiarity with various machine learning algorithms including regression, classification, clustering, and dimensionality reduction.
Good understanding of deep learning architectures such as CNNs and RNNs.
Strong analytical and problem-solving skills.
Ability to communicate effectively, both verbally and in writing, to convey complex technical concepts to non-technical stakeholders.
Exposure to reporting tools using Tableau.