Learn the work behind Data, AI & Forward Deployed Engineering
Practical explanations, career decisions and reproducible workflows. Read the reasoning, inspect the evidence and follow the next skill into a real programme.
Mandi price volatility: modal prices, returns and seasonal bands
To measure mandi price volatility, take the daily modal price for one commodity in one market, compute log returns between consecutive trading days, and report the rolling standard deviation of those returns (for…
Managerial round questions for data analysts, with answers
The managerial round for an analyst role is usually taken by the person you would report to, and it tests whether they could hand you a vague, urgent request on a busy Tuesday and trust what comes back. Expect questions…
Mainframe developer to FDE: COBOL copybooks meet AI pipelines
Yes, a mainframe developer is a credible candidate for senior forward deployed engineering (FDE) work, because banks, insurers and government systems that want AI still keep their records of truth in COBOL and VSAM, and…
ML engineer role in India: shipping models, not just training them
A machine learning engineer takes models from experiment to production and keeps them working: building feature pipelines, serving models behind APIs or batch jobs, testing and releasing new versions, and monitoring…
OTIF at order-line level: why on-time and in-full must be one test
OTIF (on time, in full) is the percentage of order lines where the entire ordered quantity was delivered on or before the promised date. Calculate it line by line: sum the quantity delivered by the promised date…
Logical reasoning in placement tests: six patterns, worked answers
Logical reasoning sections in placement tests reuse a handful of patterns: number series, coding-decoding, blood relations, direction sense, seating arrangements and syllogisms. Each has a first move that makes it…
LLM and generative AI interview questions, a mock round
Generative AI interviews for applied roles ask how large language models produce text and how you would build something dependable on top of them. The recurring themes are tokens and context windows, sampling settings…
LLM application developer: contracts, tools and failure handling
An LLM application developer builds software features that call a large language model as one component: sending it structured inputs, validating what comes back, calling tools on its behalf, and handling the many ways…
Three LLM portfolio pieces, specified with acceptance criteria
The LLM portfolio pieces that impress reviewers are specified like real work: a named user, defined inputs and outputs, acceptance criteria that can pass or fail, and published evidence that they did. Below are three…
LinkedIn for career switchers into data: reframe the old job honestly
A career switcher's LinkedIn profile should keep every past job title exactly as it was, rewrite the description under each role to show the data you handled and the decisions it fed, and add a clear bridge in the…
LinkedIn headline and About section for data analysts: worked drafts
A good LinkedIn headline for a data analyst names the role you do or want, the two or three tools you use most, and the kind of business problem you work on, for example "Data Analyst | SQL, Power BI | Retail pricing…
LinkedIn in college: what to post, whom to connect, what to skip
In college, LinkedIn is most useful as a public record of your work and a way to stay in touch with people you've actually met. Connect steadily with classmates, seniors, alumni in data roles and speakers you've heard…
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