
Intuitive Machines Data Analyst Case Interview — Lunar Telemetry Anomaly Analysis
What this case covers: A fast-paced, mission-operations–style working session that mirrors Intuitive Machines’ environment. You’ll analyze a realistic, time-series dataset modeled on lunar lander telemetry (e.g., propulsion tank pressures, power bus voltage, IMU rates, temperatures) and identify, quantify, and communicate an anomaly observed during a descent burn. The case emphasizes structured problem solving under time pressure, clear communication to non-analysts, and practical SQL/Python fluency, reflecting the company’s fast-moving interview cadence (typical flow: brief HR screen → virtual panel deep-dive → onsite with hiring manager). Expect culture-alignment prompts such as why space excites you and how you handle intense milestones, which are commonly reported in candidate experiences. ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com), [indeed.com](https://www.indeed.com/cmp/Intuitive-Machines/interviews?utm_source=chatgpt.com)) Format (70 minutes total): - 5 min — Brief: mission context, data dictionary, success criteria. - 35 min — Working session: explore data; build a minimal, reproducible analysis in SQL and/or Python (pandas); produce plots (downsampling, rolling stats); create a simple anomaly rule (e.g., threshold or z-score) and compute time-on-threshold and affected subsystems. - 15 min — Readout to hiring manager + engineers: walk through approach, findings, and trade-offs; make a go/no-go recommendation for the next burn with assumptions and risks. - 10 min — Q&A and culture fit: discuss lessons learned, documentation practices, and how you’d productionize near-real-time telemetry monitoring ahead of a mission milestone. This aligns with the company’s lunar-surface/CLPS mission focus and Houston-based operations culture. ([en.wikipedia.org](https://en.wikipedia.org/wiki/Intuitive_Machines?utm_source=chatgpt.com)) What interviewers assess: - Analytical framing: ability to turn ambiguous flight data into testable hypotheses; prioritization under a time box. - Technical execution: correct joins/window functions in SQL; tidy, reproducible Python; numerics for time-series (resampling, interpolation, windowing); clear, decision-ready visuals. - Decision support: crisp narrative for operators/managers; quantified risk and mitigation. - Collaboration and communication: working with a mock mission director and systems engineer; handling pushback. - Space-mission motivation and resilience during peak periods. Reports indicate a quick process with manager involvement and direct questions about space motivation and workload expectations. ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com), [indeed.com](https://www.indeed.com/cmp/Intuitive-Machines/interviews?utm_source=chatgpt.com)) Deliverables you’ll produce: - A short notebook or SQL script with comments and assumptions. - 2–3 plots that reveal the anomaly and its impact. - A one-page briefing (bullet points) with root-cause hypotheses, confidence level, and next steps (e.g., additional sensors to check, thresholds to tune, alerting logic). Allowed tools: SQLite/CSV (provided), Python (pandas/matplotlib or seaborn), or a spreadsheet if preferred. No internet needed. Data and a minimal schema are provided. Sample prompts you may encounter: - “Write a SQL query to segment the burn phases and compute cumulative time above pressure threshold by phase.” - “In Python, resample 50 Hz data to 5 Hz, interpolate missing values safely, and flag spikes using a rolling Z-score.” - “Propose a lightweight dashboard for mission control during descent; what metrics and alerts are MVP vs. nice-to-have?” - “Outline a path to productionize this analysis for near-real-time monitoring.”
8 minutes
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About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
3/5
Interview Tips
• Research the company thoroughly
• Practice common questions
• Prepare your STAR method responses
• Dress appropriately for the role