AI / ML Data Scientist I

Nielsen’s interview process typically starts with a timed online aptitude and coding test, followed by a technical interview focusing on machine‑learning concepts, data‑pipeline design, and coding proficiency. The final HR round assesses cultural fit, communication skills, and motivation. Candidates should be prepared for scenario‑based questions and demonstrate a clear problem‑solving approach.

Nielsen promotes a data‑first culture where curiosity and collaboration are encouraged. Employees benefit from structured learning programs, mentorship, and a balanced work‑life environment that supports hybrid schedules. The company’s global footprint offers exposure to diverse projects and rapid career growth.

Graduation (B.Tech/B.E., B.Sc., B.Com, BCA, or equivalent) in Data Science, Statistics, Engineering, Applied Mathematics, Operations Research, Information Sciences or any related biological/physical science. Minimum 0-3 years of relevant experience. Academic performance of at least 60% CGPA/percentage. No active backlogs at the time of application.

Round 1: Online aptitude & coding test → Round 2: Technical interview (ML concepts, case studies, coding) → Round 3: HR interview (fitment, communication, motivation)

Nielsen is a global measurement and data analytics company that helps brands, retailers and media companies understand consumer behavior across platforms. With a presence in more than 100 countries, Nielsen combines deep industry expertise with cutting‑edge technology to deliver actionable insights. In India, Nielsen’s Bengaluru hub serves as a centre of excellence for advanced analytics, AI, and machine learning, driving innovation for clients ranging from FMCG giants to digital advertisers. The company prides itself on a collaborative culture, continuous learning, and a strong emphasis on data‑driven decision making. The role of AI / ML Data Scientist I is designed for fresh graduates or early‑career professionals who are passionate about turning raw data into strategic solutions. As a member of the Analytics & Insights team, you will work in a hybrid setting, collaborating with cross‑functional stakeholders, including product managers, engineers, and business analysts. Your primary mission will be to design, develop, and operationalise machine‑learning models that enhance Nielsen’s measurement frameworks for publishers, advertisers, and agencies. Key responsibilities include: 1. Build and maintain end‑to‑end measurement and planning solutions for media and advertising clients. 2. Identify AI opportunities in existing projects and prototype innovative models. 3. Develop reproducible data‑science pipelines, from data ingestion to model deployment. 4. Deploy, monitor, and optimise models in cloud environments (AWS/Google Cloud). 5. Create and maintain documentation, code repositories, and version‑controlled workflows using GitLab. 6. Collaborate with product and engineering teams to productionise analytics solutions. 7. Conduct exploratory data analysis, feature engineering, and dimensionality reduction. 8. Design dashboards and visualisations using Spotfire or Tableau to communicate insights. 9. Troubleshoot production issues, address data quality escapes, and implement fixes. 10. Contribute to research on audience measurement methodologies, including bias reduction and data integration. The tech stack revolves around Python, PySpark, SQL, AWS services, GitLab, JIRA, Confluence, and visualisation tools like Tableau/Spotfire. Strong statistical knowledge and familiarity with machine‑learning libraries (scikit‑learn, TensorFlow/PyTorch) are essential. Nielsen offers a clear growth path: junior data scientists can progress to senior roles, lead analytics projects, and eventually move into data‑science management or specialised research positions. The company invests heavily in employee development through internal training, certifications, and exposure to global best practices. Why join Nielsen? You will be part of a world‑renowned analytics leader, work on real‑world problems that shape media and consumer markets, and gain experience with large‑scale data pipelines in a supportive, innovation‑driven environment. The hybrid model provides flexibility while fostering team collaboration, and the company’s commitment to continuous learning ensures you stay ahead in the fast‑evolving AI/ML landscape.