Machine Learning Engineer · Phone Screen · Technical Discussion

Intuit — Machine Learning Engineer ✅ Passed

Level: Senior-Level

Round: Phone Screen · Type: Technical Discussion · Difficulty: 3/10 · Duration: 60 min · Interviewer: Neutral

Topics: Machine Learning, Data Analysis, Weather Data, Energy Consumption, Ranking Algorithms

Location: San Francisco Bay Area

Interview date: 2025-12-01

Summary

I had a technical phone screen focused on analyzing weather data to estimate energy usage. The task involved ranking days by likely energy consumption based on temperature and precipitation data.

Details

I was given a dataset containing temperature and precipitation data for a single city over three years. The dataset included columns for date, maximum temperature (maxt), minimum temperature (mint), average temperature (avgt), and average precipitation (precip).

My goal was to help customers estimate their energy costs for heating and cooling based on the weather data. Specifically, I needed to rank the days by likely energy usage in descending order.

I was allowed to use common libraries and API references. The input data was provided in a file named input.csv within a Jupyter Notebook environment.

The interviewer asked if I would draft a Python notebook solution that:

1. Loads input.csv.
2. Defines a proxy formula for heating/cooling energy usage (e.g., based on the deviation of avgt from a comfort temperature like 65°F).
3. Ranks the days by estimated energy usage.