Private-Equity Data Scientist — End-to-End Analytics

The Hollister Group; Lynn (MA); Hybrid; USD 138,000 - 276,000; Full time

Job summary

The Hollister Group in Lynn, MA is seeking an independent data scientist to support private equity investment decisions through applied data science and machine learning. You will build predictive models from large datasets, develop proprietary analytics, and translate findings into actionable insights for stakeholders to inform strategy.

You will lead end-to-end analytics projects, incorporate alternative data sources, and perform competitive analyses.

Qualifications

• Bachelor's degree or equivalent in a highly quantitative field.
• Experience with machine learning or related areas.
• Proficiency in Python programming.
• Experience handling terabyte-scale datasets and collaborating to build models.
• Interest in venture capital and data-driven decision making.
• On-site presence three days per week at the client office in New York City.

Responsibilities

• Build predictive models and analytics from large datasets using advanced queries, data filters, and dashboards.
• Translate data findings into actionable insights for stakeholders to inform strategy.
• Lead end-to-end analytical projects from concept to deployment and monitoring.
• Incorporate alternative data sources to identify top investment opportunities for clients.
• Perform competitive analyses to assess investment viability.
• Leverage portfolio company data to improve outcomes such as forecasting and enrichment.

Skills

Machine learning

Python programming

Data analysis

Quantitative degree

Education

Bachelor's degree

Tools

Python

Job description

The Hollister Group in Lynn, MA is seeking an independent data scientist to support private equity investment decisions through applied data science and machine learning. You will build predictive models from large datasets, develop proprietary analytics, and translate findings into actionable insights for stakeholders to inform strategy.

You will lead end-to-end analytics projects, incorporate alternative data sources, and perform competitive analyses.