Data Scientist - AI Ops

This opening is a good fit for graduates who enjoy working with data more than talking about it. Wolters Kluwer is giving freshers a chance to learn data preparation, analysis and model training inside a team that ships AI-powered products to professionals worldwide.

Why This Role Matters

Many people picture data science as model building. In practice, most of the value comes from clean, reliable data. In this role you sit at the start of that pipeline. The datasets you gather and validate feed the models that senior data scientists train, so careless preparation weakens everything built on top of it.

Wolters Kluwer serves customers in over 180 countries, and its customers use its tools for high-stakes decisions. The company says nearly 70% of its digital revenue now comes from AI-powered solutions. That is why the AI Ops label on this job matters: the team supports the operational side of AI, where data quality and consistency decide whether a model can be trusted.

Hybrid Entry Level Full-Time

A Typical Day In This Role

Expect your days to move between three kinds of work. The first is data collection and standardization, where you pull data from several sources and bring it into one consistent format. The second is cleaning and preprocessing, which means handling missing values, duplicates and formatting problems. The third is early analysis, where you look for patterns and report them in plain language to senior colleagues.

You will also help maintain databases and data repositories, document each step you follow, and join data validation and integrity checks. Documentation sounds minor, but it is how teams repeat results and trace errors back to their source.

Skills Snapshot For Applicants

The posting lists basic skills, so you do not need years of experience. Here is how the listed skills translate into what you should be able to show.

Python, SQL and Excel are all listed skills , so prepare all three rather than only one.

Hands-On Work You Will Pick Up

The role is described as supportive, which works in your favor: you get exposure to several data science tasks without owning the whole outcome.

You will take part in training machine learning models with guidance, which is the right way for a fresher to meet this work. You will see how prepared data feeds a model, how results are checked, and why validation steps exist. You will also prepare simple visualizations and reports and learn what a non-technical reader needs to see in a chart. Staying current with data science trends and tools is part of the role, so curiosity counts here.

Python SQL Excel Data Visualization Statistics

Hybrid Work And Team Culture

The role is based at Smartworks in Pune and follows a hybrid pattern of 8 days per month in the office. You will collaborate with colleagues to understand data requirements and goals, so asking good questions early saves everyone time. Wolters Kluwer also runs a global well-being program, and Forbes has listed it among America’s Best Large Employers for 2026.

Interview Rules To Know Beforehand

Wolters Kluwer states its interview practices openly, and you should read them before you apply. Candidates are asked to attend without AI tools or external prompts, to remove virtual backgrounds, and to expect in-person interview rounds. You may be asked to appear onsite at a Wolters Kluwer office. The company says AI-generated responses or third-party help during interviews are grounds for disqualification.

Practice explaining your own projects out loud, because this interview is designed to hear your voice and not a polished script.

Likely Hiring Flow

flowchart LR
A[Online Application] --> B[Resume Screening]
B --> C[Technical Discussion]
C --> D[Onsite Interview]
D --> E[Offer]

How To Prepare Before Applying

Start with SQL, because most entry-level data roles test it first. Practice joins, GROUP BY, subqueries and window functions on a sample database. Next, refresh Python data handling: reading CSV files, cleaning columns, merging tables and plotting a basic chart.

Then revise basic statistics. Be ready to explain concepts like standard deviation, correlation and the difference between a sample and a population in plain words. Finally, prepare one small project that shows the full flow from raw data to cleaned data to a simple insight. A project with documented steps will say more about you than a long list of tools.

Resume Tips For This Role

Describe your projects through the lens of data quality. Instead of saying you analyzed a dataset, say how many sources you combined, which issues you fixed and what the final output showed. Mention SQL, Python and Excel in a clearly visible skills section, and link your GitHub or portfolio if you have one. Keep the resume to one page. Because the role stresses communication and documentation, make sure your own writing is clear and free of errors.

Long-Term Career Direction

This position is a base for growth across the data field. With experience, people in similar roles move toward data science, machine learning engineering, analytics engineering or data engineering. The responsibilities in this posting cover the groundwork for each of those paths, so the first year is about learning and building a strong foundation.

Keywords for Resume

Data Science • Data Cleaning • Data Preprocessing • Python • Pandas • NumPy • SQL • Excel • Data Visualization • Basic Statistics • Machine Learning • Data Validation • Data Collection • Reporting • AI Operations • Documentation • Team Collaboration

Final Thoughts

This is a sensible first role for graduates who like working with data and want to learn how AI products are supported behind the scenes. The skill requirements are basic, the learning scope is wide, and the interview expects honest, independent answers. If your fundamentals in Python, SQL and statistics are in place, it is worth applying.