Job Description

Job Description:

We are seeking a hands-on Data Scientist (CSW) to develop analytics and machine learning solutions using large and complex datasets. This role emphasizes data analysis, feature engineering, and model development, with a strong focus on delivering practical, production-ready insights.

The ideal candidate will collaborate closely with engineering and product teams, as well as a senior PhD-level Lead Data Scientist, to design and implement scalable analytical solutions.

Experience in utility or energy data (electric, gas, water, AMI, IoT, or time-series data) is highly desirable. Alternatively, candidates with strong experience in demography are also encouraged to apply.

Key Responsibilities

• Analyze large datasets to identify patterns, trends, and anomalies.
• Develop, validate, and optimize statistical and machine learning models.
• Deliver actionable insights to support product, operational, and engineering decisions.
• Collaborate with engineering teams to support deployment and operationalization of models.
• Perform data cleaning, preprocessing, and feature engineering.
• Design and evaluate experiments as needed.
• Translate business and operational challenges into analytical solutions.
• Communicate findings effectively to both technical and non-technical stakeholders.
• Maintain clear documentation and ensure knowledge transfer to internal teams.
• Follow best practices in development, including version control and reproducibility.

Required Qualifications

• Proven experience in data science or advanced analytics roles.
• Strong proficiency in Python (e.g., pandas, NumPy, scikit-learn).
• Solid foundation in statistics and machine learning concepts.
• Experience working with SQL and structured datasets.
• Ability to work independently and quickly adapt in a CSW (Contingent Staff Worker) environment.

Preferred Qualifications

• Experience with utility, energy, or industrial datasets (electric, gas, water, AMI, IoT).
• Background in demography or population analytics.
• Experience with time-series analysis and anomaly detection.
• Familiarity with big data platforms (e.g., Spark, Databricks, or cloud-based data systems).
• Experience deploying or supporting models in production or near-production environments.

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