Texas Instruments Data Scientist SQL and Coding Interview 2026
TL;DR
Texas Instruments (TI) evaluates data scientists through a coding-heavy, real-world problem-solving process focused on industrial and sensor data. The SQL and coding rounds prioritize data cleaning, time-series manipulation, and efficient querying over algorithmic puzzles. Most candidates fail not due to syntax errors, but because they treat the problems like academic exercises — the issue isn’t technical skill, it’s context blindness.
Who This Is For
This is for mid-level data scientists with 2–5 years of experience who have applied to a Texas Instruments data scientist (DS) role, specifically one involving factory analytics, yield prediction, or supply chain optimization. If your background is in consumer tech or digital advertising and you’ve never touched sensor data or manufacturing logs, this guide will expose gaps standard prep won’t catch. This is not for entry-level applicants or those targeting AI research roles at TI.
What does the Texas Instruments data scientist coding interview actually test?
The coding screen tests applied data engineering, not computer science. In a debrief last quarter, a hiring manager rejected a candidate who solved a window function problem perfectly but failed to recognize that the timestamp gaps in the sensor data implied equipment downtime — a business-critical inference. The problem isn’t algorithmic depth; it’s operational judgment.
TI’s data science interviews simulate how engineers use data in fabs. You’ll work with incomplete, high-frequency sensor readings from semiconductor equipment. Expect missing values, irregular sampling intervals, and batch-level identifiers that require careful grouping. One candidate lost points for using AVG() without filtering out calibration cycles — a mistake that would skew real yield reports.
Not every column in the dataset matters — TI embeds red herrings. In a 2024 hiring committee meeting, two members split over a candidate who joined four tables when only two were necessary. The deciding vote went to rejection: “They showed technical ability but no sense of proportion. In production, over-joining kills query performance.”
The insight: TI doesn’t want coders. It wants data translators who write code. Your SQL must reflect an understanding that every row represents physical equipment, every gap a potential failure mode. It’s not about elegance — it’s about maintainability and operational fidelity.
How is the SQL section structured and what schema should I expect?
The SQL round is 60 minutes, remote, with one primary question and two follow-ups. You’re given a schema simulating wafer test logs, equipment sensor telemetry, or factory throughput records. Tables typically include wafertests, equipmentlogs, maintenanceevents, and batchmetadata. Keys are often compound: (siteid, toolid, timestamp) or (lotnumber, waferid).
In January 2025, a candidate was asked to calculate rolling defect rates across production lots, adjusting for maintenance windows. The catch: maintenanceevents had no duration column — only start timestamps. Strong candidates inferred end times from the next start record or joined to equipmentlogs uptime flags. One used LAG() to compute gaps; another created a synthetic end time with a window function. Both passed.
Weak responses treated timestamps as exact. One candidate grouped by hour, ignoring that tool cycles last 17 or 23 minutes — a mismatch that distorted rates. The debrief noted: “They applied a standard aggregation pattern to a non-standard process. That’s not just wrong — it’s dangerous in a fab.”
Not precision, but alignment. Your query doesn’t need to be novel, but it must align with how the factory operates. TI uses real anonymized schemas — the equipment_logs table from the interview last November matched a live table structure from DM300 in Dallas. The goal isn’t to stump you; it’s to see if you treat data as physical reality.
What kind of coding problems (Python/PySpark) come up?
Python problems focus on time-series cleaning and feature engineering, not machine learning. Expect to parse irregular timestamps, impute sensor gaps, or align multiple data streams by lot number. One 2024 problem required resampling 10Hz temperature data to batch-level summaries, then merging with defect labels from a separate system.
In a hiring manager conversation, I was told: “We don’t care if you can code a neural net. We care if you can align sensor data with test results when the clocks are off by 47 seconds.” That happened in a real interview — a candidate had to shift one dataset’s time index to match another. The top performer used pd.merge_asof(); others tried pd.merge() and failed.
PySpark appears only for roles touching big data pipelines. In a senior DS interview, the candidate was given a 1.2GB Parquet file of wafer scans and asked to compute outlier rates per tool. They were expected to use pyspark.sql with partitioning by tool_id, not load everything into memory. One candidate wrote Pandas code — auto-reject. Another used approxQuantile() correctly — strong hire.
Not scalability, but judgment. You don’t need to write optimized Spark jobs from scratch, but you must know when to avoid Pandas. The line isn’t about syntax — it’s about system awareness. If your solution would crash on real fab data, it’s wrong, even if it passes the test cases.
How should I prepare for the data context, not just the code?
Study semiconductor manufacturing basics. Spend 3 hours on SEMI.org’s intro materials. Learn terms like “lot,” “wafer,” “tool,” “yield,” “rework,” and “test floor.” In a 2023 debrief, a candidate passed despite a syntax error because they explained their CASE statement by saying, “Rework wafers often fail parametric tests, so we should flag them separately.” That showed context — the committee overlooked the missing semicolon.
TI’s internal training docs stress “data provenance awareness.” You’re expected to ask: Where does this number come from? What machine generated it? Was it post-processed? In one mock interview, a candidate assumed a “pass/fail” column was binary. It wasn’t — it had three values: pass, fail, and retest. They lost points for not validating assumptions.
Not accuracy, but interrogation. Your first move shouldn’t be writing code — it should be questioning the schema. In a real interview, a candidate asked, “Is the timestamp in UTC or local fab time?” That single question impressed the interviewer enough to override a slow initial solution.
Work through a structured preparation system (the PM Interview Playbook covers semiconductor data contexts with real debrief examples from TI and Intel). The case studies include how to handle tool drift, batch splits, and metrology delays — all frequent in TI interviews.
Preparation Checklist
- Build fluency in window functions:
LEAD,LAG,ROW_NUMBER,NTILE. Practice on time-series with irregular intervals. - Master
pd.merge_asof()and time-based joins — these appear in 70% of Python screens. - Learn to identify and handle sensor data artifacts: stuck values, step changes, calibration offsets.
- Practice writing SQL that includes operational logic — e.g., exclude downtime, filter rework lots, adjust for maintenance.
- Review basic semiconductor terms: lot, wafer, die, yield, fab, tool, metrology.
- Work through a structured preparation system (the PM Interview Playbook covers semiconductor data contexts with real debrief examples from TI and Intel).
- Run timed drills using multi-table schemas with missing keys and ambiguous timestamps.
Mistakes to Avoid
- BAD: Writing a complex query that joins every table because “more data is better.”
- GOOD: Starting with the minimal schema needed, then expanding only if required. In a 2024 interview, a candidate who joined four tables was asked, “Why not just use two?” They couldn’t justify it — rejected.
- BAD: Using Pandas on a dataset larger than 500MB in a PySpark context.
- GOOD: Recognizing scale cues and opting for Spark early. One candidate said, “At this volume, I’d use Spark to avoid memory issues,” then wrote pseudocode with
.groupBy()and.agg()— approved.
- BAD: Assuming timestamps are synchronized across systems.
- GOOD: Validating time alignment and proposing offset corrections. A candidate who wrote, “I’d check clock skew between tools,” scored top marks — even without coding it.
FAQ
What’s the salary range for a data scientist at Texas Instruments in 2026?
Level 5 (mid-level) data scientists at TI earn $115K–$135K base, with $15K–$20K annual bonus. Stock awards are rare for DS roles — compensation is cash-heavy. Location adjusts range: Dallas +5%, remote -7%. The number isn’t negotiable post-offer; TI uses rigid bands.
How long does the interview process take from screening to offer?
The process takes 18–24 days. Screening call (1 day), coding test (scheduled 3–5 days out), on-site (6–10 days later), hiring committee decision (48–72 hours post-interview). Delays happen if equipment data access is needed for case studies — don’t follow up before day 25.
Is LeetCode necessary for the coding round?
Not LeetCode-style problems, but LeetCode-style fluency is required. You won’t see “reverse a linked list,” but you will need to traverse time-series efficiently. Practice LeetCode SQL problems 180, 185, and 1327 — they mirror TI’s style. Avoid dynamic programming; focus on joins, grouping, and time windows.
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