Preparing for an interview at Granular requires a shift in mindset compared to traditional, slow-paced data science interviews. The evaluation process is designed to test how you perform under pressure and how quickly you can deliver a working solution.

Execution Speed & Prototyping – Interviewers at Granular want to see how fast you can go from raw data to a working model. You will be evaluated on your ability to bypass unnecessary perfectionism in favor of building a solid, functional baseline model quickly.

Pragmatic Problem Solving – You will often be handed datasets with little to no documentation. Your ability to make reasonable, logical assumptions about the features, document those assumptions, and proceed with your analysis is highly valued.

Data Manipulation Proficiency – You must be incredibly fluent in SQL and Python (specifically Pandas). There is no time to look up basic syntax during the timed challenges; your coding mechanics must be second nature.

Communication Under Pressure – During the onsite rounds, you will be coding and modeling live with other data scientists. You need to be able to explain your thought process, justify your shortcuts, and receive feedback constructively while keeping an eye on the clock.

The interview process at Granular is structured to assess your hands-on technical capabilities immediately. It is a highly streamlined, fast-moving pipeline that prioritizes practical coding and modeling over lengthy behavioral conversations. Expect a process that moves quickly from your initial application to the final decision.

The journey begins with a rapid technical screening, often sent shortly after your application is received. This is designed to filter for candidates who possess the core coding and modeling speed required for the role. If you pass this initial hurdle, you will move to a brief conversational screen with a hiring manager to discuss your background and alignment with the team's goals, followed immediately by a rigorous onsite loop.

The onsite interview is highly technical and highly compressed. It typically consists of three consecutive, one-hour sessions where you will work live on data challenges. Two of these sessions are dedicated to rapid modeling (often focusing on classification and time-series problems), while the third focuses on core SQL and Pandas data manipulation.

The timeline above illustrates the rapid progression of the Granular hiring pipeline, highlighting the immediate transition from application to active technical screening. Candidates should manage their preparation energy by focusing heavily on speed-coding and baseline modeling exercises early in the process. Because the onsite loop is highly concentrated, building physical stamina for back-to-back coding sessions is essential.

To succeed in the Granular interview loop, you must understand exactly what is being evaluated in each core session. The interviewers are looking for specific behaviors and technical choices during these high-pressure rounds.

Rapid Baseline Modeling

This area evaluates your ability to quickly ingest a dataset, perform minimal necessary cleaning, and train a functional machine learning model within a highly constrained timeframe (typically 45 to 60 minutes).

Be ready to go over:

• Baseline Selection – Choosing simple, robust algorithms like Linear Regression, Logistic Regression, or Decision Trees that train instantly and are easy to interpret.
• Rapid Feature Engineering – Implementing quick imputation strategies and basic one-hot encoding without over-complicating the pipeline.
• Evaluation Metrics – Selecting and calculating the correct metric (e.g., F1-score, RMSE) to quickly prove your model is better than random guessing.
• Advanced concepts (less common) – Hyperparameter tuning via grid search (usually discouraged due to time limits, but good to mention conceptually) and regularization techniques like Lasso or Ridge.

Example scenarios:

• "You are given a dataset of historical farm equipment usage. Within 45 minutes, build a model to predict whether a machine will fail in the next 10 days."
• "Train a baseline classifier on a dataset with 20 anonymous features, and justify why you chose a random forest over a logistic regression in this scenario."

Time-Series Forecasting & Classification

This evaluation area focuses on your ability to handle sequential, time-dependent data, which is a core component of agricultural modeling at Granular.

Be ready to go over:

• Temporal Splitting – Ensuring you split your training and validation data chronologically to prevent looking into the future.
• Lag Features – Creating rolling averages, differences, and lag variables to capture temporal trends quickly.
• Handling Seasonality – Identifying cyclical patterns in agricultural cycles and incorporating them into your model structure.

Example scenarios:

• "Given a dataset of daily temperature and soil moisture readings, build a model to classify whether a crop is under water stress, ensuring your validation strategy prevents data leakage."
• "Wrangle a highly irregular time-series dataset of satellite imagery observations into a structured format suitable for a classification algorithm."

SQL & Pandas Data Wrangling

This session tests your ability to manipulate, clean, and aggregate data efficiently using standard querying and scripting tools.

Be ready to go over:

• Complex Joins - Joining tables on multiple keys, handling many-to-many relationships, and resolving null values post-join.
• Window Functions - Using functions like ROW_NUMBER(), LEAD(), and LAG() in SQL to perform sequential calculations.
• Pandas Grouping & Aggregation - Utilizing .groupby(), .pivot_table(), and custom aggregation functions to summarize large datasets.

Example scenarios:

• "Write a SQL query to find the top three most profitable crops for each state, based on a multi-table schema containing yield, pricing, and acreage data."
• "Using Pandas, take a messy dataset of sensor readings, remove outliers that fall outside three standard deviations, and aggregate the remaining data to an hourly level."