Data Scientist

at  Manhattan Strategy Group

MSG is a small-disadvantaged, social sciences consulting firm specializing in research and evaluation, policy analysis, and technical assistance (TA). We pride ourselves on rigorous and innovative qualitative and quantitative data collection, management, analysis, and reporting. Our work is with multiple Departments, including education, labor, homeland security, and health and human services. This position will support the firm’s Research and Evaluation service line.
The successful candidate will have a solid foundation in quantitative methods. They will join a dynamic team of researchers from a variety of disciplines working together to design, develop, and implement rigorous research for USDA and other clients. The Data Scientist will be responsible for managing, understanding and analyzing client data – including text mining, developing predictive systems, risk scoring, creating efficient algorithms, data quality improvement and other related activities.
This individual will be able to implement statistical analyses of gathered extant and open source, data to create analytic metrics and tools to support research projects.

QUALIFICATIONS

• Master’s Degree with 5 years of experience.
• Solid foundation with data platforms and applications such as DataBricks.
• Experience programming in R, Python.
• Experience with distributed processing systems for big data (e.g., Pyspark, Spark) and machine learning packages (e.g., Apache Spark)
• Experience with data visualization platforms such as Tableau, Plotly, Streamlit, or Power BI
• Experience with medium-to-large data sets (>1m rows)
• Experience with data and table modernization a plus.
• Excellent written and verbal communication skills.

• Apply AI/ML expertise and creativity to apply existing methods and develop new ones to solve these problems in a practical and scalable way.
• Research and propose appropriate models/techniques, as well as implement, train and evaluate models.
• Effectively work across projects, as necessary.
• Collaborate daily with a diverse team of researchers.
• Build and analyze large datasets of extant data.
• Participate in business development.