Growth Generalist (8 Month Contract)

About the role

These teams define user acquisition and retention methods within an AI context to address evolving demands.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Teams analyze experimental results to refine tests and guide product adjustments for audience targeting.

Teams work through open-ended challenges to generate actionable insights and inform strategy.

Immediate availability for a full eight-month contract with a possible extension meets team timelines. Teams prepare to start work promptly while maintaining flexibility for extension to satisfy project schedules.

Requirements

You hold a bachelor's degree, and you The degree requirement is confirmed You navigate ambiguous problems and conduct creative exploration to solve open-ended questions that lack predefined paths. Comfort with undefined challenges is essential for success in this role to drive innovative solutions.

You remain immediately available for full-time work across an eight month period with a possible extension to meet team timelines. Immediate availability for the full eight-month contract is required to align with project timelines.

Practical notes

Some companies give a take-home analysis.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Nice to have

Data professionals turn raw information into decisions, and analysts query databases while building dashboards to support this work. General skills in statistics, coding, and communication support success in data roles across modern organizations.

Skills & tools

Modern companies rely on data teams that span analysts, data scientists, and data engineers to turn information into decisions. General skills in statistics, coding, and communication support success in data roles that evolve with new tools and methods.

About the company

About the location

San Francisco offers dense tech networks and proximity to innovation hubs. The city supports a dynamic environment for fast-adapting teams working on real-time intelligence.

Good to know

Data roles convert information into decisions through analysis and modeling, with professionals often specializing in analytics, machine learning, or infrastructure. Continuous learning and clear communication help professionals advance quickly in this field by translating numbers into decisions. Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.