Data Scientist, Richard, Wayne & Roberts, VA, US

NO C2C. Cannot typically sponsor. This is a direct-hire role - no contract periods available. You must be eligible and authorized to work in the US.

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A global energy infrastructure company is seeking a highly skilled Data Scientist to design, build, and deploy machine learning and optimization models in a distributed computing environment. This role involves full lifecycle model development—from data preparation to deployment—and requires a strong foundation in data engineering, Python programming, and Spark (PySpark) .

You’ll work in close partnership with technical and business stakeholders to ensure that data solutions provide measurable impact. This is an onsite role based in Arlington, VA , reporting to the Director of Business Intelligence, and embedded within the organization’s enterprise applications team.

Key Responsibilities:

• Own and execute end-to-end data science workflows , from requirements gathering to model deployment and evaluation.
• Transform, clean, and join large datasets to make them suitable for modeling—no data, no model.
• Build, test, and deploy machine learning, simulation, and optimization models that drive real business value.
• Deliver production-ready code that is modular, scalable, and minimizes technical debt.
• Engage with subject matter experts to understand and solve targeted operational or strategic problems.
• Contribute to the design of automated data science pipelines and continuous improvement of infrastructure.

Qualifications:

Required:

• Bachelor's degree in a quantitative or technical field and 2+ years of experience, or a Master’s in Data Science or related discipline.
• Proficiency in Python and deep familiarity with one or more data manipulation libraries (e.g., Pandas, Polars, Spark ).
• Experience preparing data for modeling: including ingestion, cleansing, transformation, aggregation, and profiling.
• Ability to work autonomously, communicate technical concepts clearly, and manage multiple tasks in a fast-paced setting.

Preferred:

• Hands-on expertise in Spark (PySpark) using the Spark SQL API (not Pandas-on-Spark).
• Ability to build data visualizations using libraries like Seaborn, Plotly, Matplotlib, Altair , etc.
• Familiarity with functional programming paradigms , unit testing, and software engineering best practices.
• Experience with simulation modeling (e.g., system dynamics, agent-based modeling, or discrete event simulation).
• Exposure to distributed ML/DL libraries (e.g., SparkML, PyTorch).
• Familiarity with open-source Python optimization frameworks.