Impact Analytics Data Analyst (Python/SQL)

TalentHop; United States; On-site; USD 87,000 - 107,000; Full time

Benefits offered by this job

Major medical insurance

PTO 20 days

Volunteer days

Holidays 13

401(k) with match

Parental leave 12 weeks

One Medical membership

Fertility & hormonal health benefits

Job summary

TalentHop seeks an Impacts Analytics Data Analyst to measure and model the effects of interventions on justice-involved populations. You will build statistical models in Python/SQL, translate insights to stakeholders, and help shape product strategy with reusable analytics processes.

The role partners with growth, fundraising, and research teams to track outcomes, develop automated data workflows, and evaluate experimental designs within a cross-functional environment.

Qualifications

• Minimum 2 years of experience analyzing complex datasets.
• Strong proficiency in SQL and Python (Pandas).
• Ability to communicate and collaborate with diverse partners.
• Experience with data visualization and reporting tools.

Responsibilities

• Build statistical models using Python and SQL to assess historical and forecast future impacts of interventions
• Collaborate with growth and fundraising teams to share impact analysis with varied stakeholders
• Develop automated workflows for ongoing tracking of outcome improvements
• Partner with impact tracking and research leads to guide product strategy using data insights
• Define and implement metrics for product usage and impact within an aggregated metrics framework
• Advise data analysis and science teams on experimental design and rollout evaluation methodologies

Skills

SQL

Python (Pandas)

Communication

Collaboration

Tools

Google Cloud Platform

BigQuery

Looker

Jupyter notebooks

Job description

TalentHop seeks an Impacts Analytics Data Analyst to measure and model the effects of interventions on justice-involved populations. You will build statistical models in Python/SQL, translate insights to stakeholders, and help shape product strategy with reusable analytics processes.

The role partners with growth, fundraising, and research teams to track outcomes, develop automated data workflows, and evaluate experimental designs within a cross-functional environment.