Use SQL, Python, or other analytical tools to query, clean, and analyze large datasets.
Analyze labor market trends across dimensions such as workforce composition, job demand, employment patterns, and wage dynamics.
Apply advanced techniques including time series forecasting, clustering, regression analysis, and machine learning algorithms (e.g., decision trees, random forests, gradient boosting, support vector machines, k-means clustering) to identify patterns and forecast labor trends.
Build and maintain scalable data and model pipelines using orchestration tools like Apache Airflow, Luigi, or Sagemaker Pipelines, and model tracking tools such as MLflow.
Collaborate cross-functionally to define problem statements, gather and analyze data, integrate models into products, and translate insights into actionable recommendations for technical and non-technical audiences.
Stay informed on advancements in labor market analytics, statistical modeling, and machine learning technologies.

Bachelor’s or Master’s degree in Data Science, Statistics, Economics, Computer Science, or a related quantitative field.
2+ years of industry experience or academic research applying statistical or machine learning approaches to real-world data problems.
2+ years of experience in labor market analytics, workforce intelligence, or related domains (preferred).
Proficiency in Python, SQL, and machine learning libraries such as Scikit-learn or PyTorch.
Demonstrated ability to write clean, scalable code and independently prototype data solutions.
Solid understanding of machine learning techniques such as classification, regression, and time-series forecasting.
Experience with cloud platforms (e.g., AWS) and orchestration tools like Apache Airflow for managing large datasets and workflows.
Strong statistical reasoning and ability to translate complex data into strategic business insights.
Excellent interpersonal, written, and verbal communication skills, with the ability to convey technical topics to diverse audiences.
Experience working with large labor market datasets (e.g., government labor statistics, survey data, or job market intelligence platforms) (preferred).
Proficiency with data visualization tools such as Tableau, Power BI, or Plotly (preferred).
Familiarity with Natural Language Processing (NLP) techniques and applications (preferred).

Data Scientist

Key skills