Data Scientist

Data Scientist at Global Connect Technologies Data Scientist at Global Connect Technologies in Raleigh, North Carolina Posted in 3 days ago.

Type:

full-time

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.