Jr Data Science Analyst

As a Jr Data Science Analyst at NielsenIQ, your primary responsibilities will include supporting clients with data science methodologies, assisting in feasibility analysis, and working closely with senior leaders to understand and address business needs. You'll also explore diverse data sets using Python or R to develop innovative solutions, collaborate with other team units, and automate existing processes for efficiency.

Candidates for the Jr Data Science Analyst role at NielsenIQ should hold a degree in Mathematics, Data Science, Statistics, or a related field. Ideally, you would have 0-2 years of experience in market research or a related area. Essential skills include problem-solving, intellectual curiosity, and proficiency in programming languages like Python or R. A solid foundation in statistics and the ability to communicate complex concepts effectively are also crucial.

For the Jr Data Science Analyst position at NielsenIQ, familiarity with programming languages such as Python and R is essential. These tools will be critical as you work on data analysis projects, develop innovative solutions, and automate processes to improve efficiency within the team.

Yes, NielsenIQ offers a hybrid work model for the Jr Data Science Analyst position, allowing for flexibility between working from home and the office. This adaptable approach helps create a balanced work environment that promotes productivity and wellness, tailoring to the diverse needs of our employees.

As a Jr Data Science Analyst at NielsenIQ, you can expect a robust benefits package that includes comprehensive health insurance, a flexible work environment, a peer recognition program, access to on-demand learning content, mentoring programs, and a chance to enjoy short Fridays. We are committed to fostering career development tailored to your personal growth.

When discussing a project, outline your role clearly, the objectives of the analysis, and the methodologies you employed. Make sure to highlight how you utilized programming languages like Python or R to handle data, perform analyses, and present your findings, showcasing the impact of your work on decision-making.

Effective prioritization is key in data roles. Talk about your approach to assessing project deadlines, complexity, and business impact. Explain how you communicate with team members and clients to align on priorities and ensure timely delivery of quality results.

Discuss specific statistical techniques like regression analysis, hypothesis testing, or A/B testing. Share an example of how you've applied these methods in past projects, emphasizing your understanding of when and why to use them for accurate data insights.

Addressing errors is crucial. Describe your method for identifying and rectifying errors, possibly discussing the tools and techniques you use for data cleaning. Highlight your commitment to accuracy and how you ensure that your final analyses are reliable.

Talk about any experience you have with data visualization tools such as Tableau or Power BI. Give examples of how you've used these tools to present data findings clearly and effectively, catering to non-expert audiences to facilitate understanding.

Share your passion for data analysis and problem-solving. You might mention the thrill of uncovering insights that drive business decisions, the joy in crafting narratives from numbers, or the continuous learning that the field offers as technology evolves.

Discuss your approach to professional development, such as following industry blogs, participating in online courses, or attending workshops and webinars. Emphasizing your commitment to lifelong learning demonstrates to interviewers your determination to stay current in the fast-paced data science field.

Effective communication is essential. Discuss your strategy for tailoring your message to different audiences, using visuals when necessary, and structuring your presentations to be clear and concise while delivering impactful insights.

Use the STAR method (Situation, Task, Action, Result) to detail a collaborative experience where you worked with team members to address a data-related issue. Emphasize your role in fostering teamwork and ensuring smooth communication and problem resolution.

Express your admiration for NielsenIQ’s commitment to innovation and diversity in its workforce. Highlight how your personal values align with the company’s mission, and specifically mention your enthusiasm for contributing to projects that leverage consumer insights to drive positive outcomes.