Data Scientist - job 2 of 3

At Experian, a Data Scientist is responsible for developing machine learning algorithms to solve complex business questions, improving existing models' performance, discovering new data sources, and creating new input features for machine learning models. Additionally, you will build end-to-end data pipelines and communicate analytical results to stakeholders.

To apply for the Data Scientist position at Experian, candidates must have a Master's degree in Machine Learning, Data Science, Computer Science, Statistics, or a related field. Additionally, experience in statistical modeling techniques, natural language processing, deep learning algorithms, and proficiency in Python, R, SQL, and SparkSQL are essential.

A Data Scientist at Experian should be well-versed in statistical modeling techniques including regression and neural networks, natural language processing, and deep learning algorithms like convolutional and recurrent neural networks. Familiarity with building end-to-end scalable ML solutions, API development, and working in a Linux environment is also important.

The pay range for the Data Scientist role at Experian is between $96,678.00 and $151,230.00. Actual pay will depend on factors such as work location, job-related skills, experience, and education. The role also includes variable pay and a comprehensive benefits package.

To apply for the Data Scientist position at Experian, visit their careers page and search for requisition number 744000047233467. You’ll find all the necessary information and steps to submit your application.

When discussing a machine learning project, highlight your specific contributions, the challenges faced, and the outcomes. Focus on your role in developing algorithms, the technologies you used, and any collaborative efforts that made the project successful.

Be prepared to discuss specific statistical modeling techniques such as regression analysis, support vector machines, or neural networks. Share instances where you applied these techniques in projects and the insights gained from them.

Discuss methods you've used to address missing data, such as imputation techniques, exclusion methods, or leveraging algorithms capable of handling such data. Tailor your answer with real-life examples.

Explain your approach to model optimization, including techniques like hyperparameter tuning, feature selection, and validation strategies. Reference any tools or libraries you've used in your optimization process.

Share specific NLP projects you've worked on, outlining the techniques applied, such as tokenization or sentiment analysis. Make sure to discuss the results and how they added value to the organization.

Explain methods you use to simplify complex data insights, like using visualizations, analogies, or layman's terms. Provide an example of a time your communication significantly impacted a business decision.

Discuss your favorite programming languages such as Python or R and the reasons for your preferences, like available libraries and community support. Mention specific libraries you prefer for data analysis.

Talk about your approach to building scalable solutions, such as optimizing code, using appropriate data structures, or deploying models in cloud environments. Include any relevant experience with scaling models in previous roles.

Share experiences with identifying bias in data, the steps you took to mitigate it, and the impact of those actions on your model's outcomes. Highlight the importance of ethical data practices.

Discuss current trends you've observed, like advancements in AI, increased automation, or ethical considerations in data usage. Share your vision on how these trends could reshape industries and your adaptability to them.