Full-Time Data Scientist

Key Responsibilities:

• Design, develop, and implement machine learning models for prediction, classification, and clustering projects.
• Apply machine learning and reinforcement learning techniques to optimize customer acquisition, retention, pricing, cross-selling, and operations.
• Explore and apply advanced AI techniques, including large language models, to business problems.
• Understand the business context of projects and identify the limitations of model predictions where necessary.
• Translate complex business problems into actionable and achievable analytics projects.
• Collaborate effectively with multi-disciplinary teams to deliver business-focused solutions.
• Utilize Python libraries such as pandas and scikit-learn for data analysis and modeling.
• Work with Spark or PySpark for large-scale data processing.

Minimum Qualifications:

• Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering, Operations Research).
• 2+ years of experience in statistical modeling and quantitative analysis in industry or full-time academic research.

Preferred Qualifications:

• Advanced degree (MS or PhD) in Statistics, Mathematics, or Quantitative Marketing with a focus on machine learning.
• Experience in the energy sector, particularly retail electricity or gas.

Additional Skills:

• Proficiency in Python and its libraries (pandas, scikit-learn).
• Experience with big data tools (Databricks, AWS SageMaker, Azure AI Studio).
• Familiarity with Linux environments, Git, and Docker containers.
• Strong attention to detail and the ability to critically analyze results before sharing.
• Excellent communication skills, with the ability to explain complex results in practical business terms.

Physical Requirements:

• Ability to analyze data and figures, transcribe, view a computer screen, and perform extensive reading.
• Occasional overtime work may be required.
• Hybrid or remote role, with time split between working from home and an open office environment with cubicle hoteling.