Financial Analyst Intern

ETC Interactive Ltd.; New York (NY); On-site; USD 28,000 - 55,000; Full time

Job summary

etc. interactive in New York City is seeking a Financial Analyst Intern to build quantitative models for launch decisions. You will transform messy data into forecasts, assess server costs, and model demand under uncertainty, collaborating with finance, data science, and engineering.

The role emphasizes probabilistic forecasting, LTV analysis, and capital allocation, with guidance toward a structured internship or academic credit arrangement.

Qualifications

• Graduate student in a quantitative field.
• Strong Python and pandas experience.
• Experience with forecasting and Monte Carlo methods.
• Ability to explain quantitative results to non-technical stakeholders.
• Comfort turning messy data into models.

Responsibilities

• Build a dynamic cloud cost model linking concurrency, geography, and bandwidth to infrastructure expense.
• Develop probabilistic launch-demand forecasts for capacity planning.
• Model lifetime value and retention by cohort and platform.
• Compute contribution margins after platform fees and costs.
• Apply statistical and ML methods to game telemetry to predict churn and segment players.
• Interest in games is welcome but not required; focus on analytical rigor.

Skills

Python

Pandas

Statistics

Forecasting

Monte Carlo methods

Model calibration

Communication of results

Education

Graduate program in financial engineering / quantitative finance / statistics

Tools

scikit-learn

statsmodels

Job description

etc. interactive is a well-funded, independent game studio based in New York City. We are a tight-knit team of passionate gamers building Special Authority, a 72-player extraction-style first-person shooter built from the ground up in Unreal Engine, launching in early 2027.

We are looking for a Financial Analyst Intern to build the quantitative models behind our launch decisions. As we approach launch, a number of our decisions sit at the intersection of finance, data science, and engineering. Rather than traditional FP&A support, we want someone who can take messy operating and player data and build models that help us decide well under uncertainty: forecasting and optimizing server costs as concurrency changes, modeling launch demand probabilistically, analyzing player lifetime value, allocating marketing capital, and stress-testing our cash runway against different launch outcomes.

Responsibilities

• Build a dynamic cloud cost model linking concurrent users, session length, geography, and bandwidth to infrastructure expense, and develop cost-per-player and cost-per-session metrics
• Develop forecasts, such as probabilistic launch-demand and peak-concurrency forecasts, so engineering can provision against predicted outcomes rather than a single management estimate
• Model player lifetime value and retention by cohort, acquisition source, and platform, and calculate contribution margin after platform fees, server costs, and payment processing
• Treat marketing spend as a constrained capital-allocation problem, measuring risk-adjusted return by channel and determining marginal rather than average historical ROAS
• Apply statistical and machine-learning methods to game telemetry to predict churn, segment players, and identify progression points where players disproportionately quit
• Interest in games is welcome but not required. We care more about analytical rigor than whether you play shooters

Qualifications

• Currently enrolled in a graduate program in financial engineering, quantitative finance, statistics, applied mathematics, economics, or a comparably quantitative field
• Strong Python, including pandas and at least one statistical or machine-learning library such as scikit-learn or statsmodels
• Working knowledge of probability, simulation, and forecasting, including Monte Carlo methods, distribution fitting, and model calibration
• Comfortable building models from incomplete and messy operating data rather than clean academic datasets
• Able to explain quantitative results to non-technical stakeholders, and to state assumptions and uncertainty plainly rather than presenting point estimates as fact

Term

10 to 12 weeks. We are glad to structure this as an internship, industry project, or independent-study engagement for academic credit, and to work directly with your program to do so.

Location

This role can be performed remotely or in person from our New York City studio, whichever suits you. Some overlap with Eastern Time working hours is expected.

Compensation

The expected pay range for this position is $20 to $40 per hour. The actual compensation offered will be determined in good faith based on the candidate's experience, qualifications, and skills.

Questions or need accommodation? Reach out to [e-mail]