Data Scientist (Raleigh) Job at Global Connect Technologies, Raleigh, NC

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  • Global Connect Technologies
  • Raleigh, NC

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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