Data Scientist

Description

We are looking for a Data Scientist to join a fast-moving IT consulting environment in Atlanta, Georgia. This role focuses on turning complex data into practical business insights, with a strong emphasis on forecasting, predictive modeling, and customer-focused problem solving. The ideal candidate combines advanced machine learning expertise with hands-on data preparation skills and can clearly explain how analytical work influences end users and business outcomes.

Responsibilities:
Build, validate, and refine forecasting and predictive models using Python and modern machine learning frameworks for business-driven use cases.
Develop analytical solutions with tools such as scikit-learn, XGBoost, LightGBM, and time-series or deep learning methods based on project needs.
Use Databricks and Apache Spark to process large datasets efficiently and support scalable model development workflows.
Prepare, transform, and organize data by writing queries, performing ETL tasks, and improving data quality for downstream analysis.
Translate technical findings into clear recommendations for clients and stakeholders, emphasizing business impact and user experience.
Partner with customer-facing teams to define problem statements, shape data-driven approaches, and deliver actionable insights in a fast-paced setting.
Apply product thinking when designing models and analytical outputs to ensure solutions align with customer needs and practical use.
Contribute domain knowledge to projects involving retail or consumer goods data, helping tailor models to industry-specific patterns and challenges.

Requirements

Strong programming ability in Python for data analysis, model development, and production-oriented workflows.
Hands-on experience creating and assessing machine learning models for predictive analytics and forecasting applications.
Practical knowledge of Azure Databricks and Apache Spark, including a solid understanding of core Spark capabilities.
Experience with time-series analysis and familiarity with algorithms such as XGBoost, LightGBM, or comparable modeling techniques.
Working knowledge of data engineering concepts, including ETL processes, SQL querying, and dataset preparation.
Ability to communicate technical concepts to non-technical audiences and explain the business implications of analytical decisions.
Background in retail, consumer goods, or closely related industries is strongly preferred.
Proven ability to work independently, adapt quickly, and succeed in client-focused environments with changing priorities.

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