Job Description: Data Scientist

Overview

Our client is seeking a talented and motivated Data Scientist to join their dynamic team. The Data Scientist will leverage advanced analytics, machine learning, and statistical modeling to support data-driven decision making across the organization. This role will play a key part in extracting insights from complex datasets to improve operational efficiency, customer experience, and strategic planning within the energy sector.

Key Responsibilities

Apply a consultative approach to data science, partnering with stakeholders to identify business problems, recommend tailored analytical solutions, and guide decision-making throughout project lifecycles.
Analyze large and diverse datasets to identify trends, patterns, and opportunities for improvement within operations.
Design, develop, and deploy predictive models and machine learning algorithms to solve business challenges.
Collaborate with cross-functional teams, including IT, engineering, and business units, to understand data needs and deliver actionable insights.
Visualize data findings and communicate results clearly to both technical and non-technical stakeholders.
Support the implementation of data governance standards and ensure the integrity and security of data.
Stay updated on emerging technologies and best practices in data science and the energy industry.

Qualifications

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
2–5+ years of experience as a Data Scientist, ML Engineer, or similar role.
Strong proficiency in Python (pandas, scikit‑learn, NumPy, SciPy, TensorFlow/PyTorch optional).
Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow).
Experience working with data warehouses, SQL databases, and cloud platforms (AWS, Azure, GCP).
Familiarity with visualization tools such as Tableau, Power BI, or matplotlib.
Experience with statistical modeling, forecasting, and optimization.
Experience in the energy or utilities industry.
Knowledge of cloud platforms (e.g., Azure, AWS, Google Cloud) and big data technologies (e.g., Hadoop, Spark).
Exposure to MLOps practices and production model deployment.

Location: This is a hybrid position based in San Antonio TX.

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