Data Scientist, Gandhi Nagar, Vijayawada
A Data Scientist role is open in Gandhi Nagar, full-time and on-site, with a salary up to ₹1,03,000 per month. The work spans the full path from raw, messy data to a model that actually informs a business decision, not just a notebook full of exploratory charts that never leaves someone's laptop.
Two and a half years of relevant experience is the expectation here, enough time to have taken at least one model from an idea through to something running in production and being watched by people who depend on its output.
The company works across a handful of internal datasets that have grown fairly disorganized over time: different formats, inconsistent naming, gaps from before certain tracking existed. A chunk of the early work in this role involves making sense of that before any modeling can happen credibly, which is less glamorous than the machine learning part but arguably more important to get right.
What the role covers
- Explore and clean raw datasets, handling missing values, inconsistencies, and the kind of messiness that never shows up in a tutorial
- Run statistical tests and build models that surface real, actionable patterns rather than noise
- Build and evaluate models, then get them deployed rather than leaving them as one-off analyses
- Present findings to stakeholders in language that doesn't require a statistics background to follow
- Recommend concrete business decisions based on what the data actually supports, not just what's interesting
The gap between a model that performs well in a notebook and one that holds up in production is real, and closing that gap is a meaningful part of the job. Data drifts, edge cases show up that never appeared in training, and a model that looked solid in testing needs monitoring once it's actually making decisions that matter.
Presenting findings gets underrated as a skill in this field. A well-built model that's explained poorly to the people funding it doesn't survive long, regardless of how accurate it actually is. Being able to translate a confusion matrix into "here's what this means for the business" is as much a part of the job as the modeling itself.
What gets someone hired
A bachelor's degree is the baseline, and the hiring team leans toward candidates from a numbers-heavy background, math, stats, or a technical program, though what someone's actually done with that training counts for more than the degree title itself. What matters most is a track record of turning Python code into a working model that made it past the laptop stage and into something an actual system relies on.
Naukri Mitra has tracked rising demand for data science talent across tier-two cities like Vijayawada as more companies decentralize their analytics teams away from the usual metro hubs, and compensation for roles like this one has moved up accordingly.
Core skills
- Strong Python skills for data manipulation and model building
- Solid statistics foundation, not just familiarity with library function calls
- SQL for pulling and shaping data directly from source systems
- Data visualization, turning results into something a non-technical stakeholder can actually use
What strengthens an application
Experience with a common machine learning framework, scikit-learn, TensorFlow, or PyTorch, will stand out clearly. Familiarity with taking a model past the notebook stage, wrapping it so a live application can actually call it, and setting up basic checks that flag when its predictions start drifting is a genuine plus, since many candidates are strong on the modeling side but haven't taken something into production themselves.
None of these extras override a weak core foundation in statistics and Python, though. The team has seen candidates with an impressive list of frameworks on their resumes struggle with a basic question about why a particular metric was chosen, and that gap shows up quickly in the interview process, regardless of how polished the tooling experience looks on paper.
Pay and what comes with it
The salary tops out at ₹1,03,000 monthly. Alongside that, the offer includes medical coverage and a provident fund contribution, with leave that starts accruing from day one. For anyone moving to Vijayawada specifically for this role, the employer covers relocation costs and helps arrange accommodation while settling in.
The team and how work gets done
This role sits within a small analytics group that works closely with product and business teams rather than operating in isolation. Findings get discussed in regular working sessions with the people who'll actually act on them, not just written up and emailed out.
That closeness to the business side cuts both ways. It means less isolation and more visibility for good work, but it also means fielding follow-up questions directly from people who won't accept "the model says so" as a complete answer. Being ready to explain a result under real scrutiny, not just present it once and move on, is part of what makes this role work well.
Projects here run on relatively short cycles, a few weeks from question to answer in most cases, rather than long research efforts with unclear timelines. That pace suits someone who likes seeing their work put to use quickly, though it does mean less room for open-ended exploration than a pure research role might offer.
Not every project produces a clean, actionable answer, and that's treated as a normal outcome rather than a failure to write up defensively. Sometimes the data genuinely doesn't support a strong conclusion, and saying so clearly is worth more to the business than forcing a pattern that isn't really there.
Applying
Send a resume along with links to any projects, whether professional work you can share details of or personal ones, that show a model taken from raw data through to a usable result. The interview includes a practical round working through a real dataset, since that reveals far more about actual capability than a set of abstract statistics questions would.
Candidates whose experience leans more toward analytics than full model deployment shouldn't rule this out automatically. Strong statistical thinking and a track record of clear, honest data storytelling matter a great deal here, and the deployment side develops quickly with the right support once someone's in the role.
