Data Scientist (NIN-DS3-15.121324)

As a Senior Data Scientist at CSG, Inc., your main responsibilities will include leveraging Python to process data for system security analysis, identifying outliers in data, and tracking systems through the Risk Management Framework lifecycle. You will also create predictive models, craft analytic charts, and develop dashboards to visualize data findings, ensuring robust data-driven decisions.

To become a Senior Data Scientist at CSG, Inc., candidates should possess a B.S. and M.S. degree in a quantitative field such as statistics, mathematics, or computer science. Professional experience of 10 years with these degrees or 6 years if holding a PhD in a quantitative discipline is required. Proficiency in tools and techniques such as Python, SQL, and Elasticsearch is also essential.

As a Senior Data Scientist at CSG, Inc., a strong command of Python is crucial, as it will be your primary language for data processing. Familiarity with R or MATLAB for statistical analysis, SQL or MongoDB for database interactions, and experience with Elasticsearch will significantly enhance your contributions to the team.

CSG, Inc. prefers candidates with significant experience in statistical analysis, as well as hands-on experience building, tuning, and testing predictive models. Familiarity with analytic frameworks such as machine learning, natural language processing, and regression analysis is also highly desirable for the Senior Data Scientist role.

No specific certifications are required for the Senior Data Scientist position at CSG, Inc. However, having advanced degrees and relevant experience in quantitative disciplines is highly valued, along with expertise in data analysis techniques and programming languages.

When answering this question, highlight specific projects where you’ve implemented machine learning algorithms. Mention the type of algorithms you used, the results you achieved, and any challenges you faced along the way. Demonstrating both your technical skills and your problem-solving abilities is key.

For this question, outline your standard approach to building predictive models. Discuss data collection, preprocessing, selecting algorithms, training, validation, and evaluation. Providing a real-life example will help to showcase your methodology effectively.

Discuss your strategies for outlier detection, such as using statistical methods or visualization techniques. You can also mention specific tools or techniques you’ve employed to effectively manage outliers and maintain data integrity.

Be sure to mention tools like Python, SQL, R, and any data visualization tools you’ve worked with, such as Tableau or Matplotlib. It’s also helpful to talk about your experience with databases like MongoDB and in tools like Elasticsearch.

Explain your process for communicating data insights to non-technical stakeholders. Discuss how you tailor your presentations and reports to ensure clarity and usability, and give examples of successful advocacy for your data insights in previous roles.

Share a detailed example of a project where you automated data analysis tasks. Explain the initial challenge, the steps you took to develop automation, and the impact it had on efficiency and accuracy in your work.

Discuss the importance of data visualization in helping stakeholders understand complex data. Mention the tools you’ve used for creating dashboards and visual reports, as well as any specific projects where visualization played a key role.

Outline your approach to statistical analysis, including your experience with various statistical tests and models. Discuss the significance of data assumptions and how you ensure the validity of your analysis.

Provide a clear and concise explanation of the Risk Management Framework. Discuss your familiarity with its stages, why it’s essential for data security, and any relevant experience you have working within this framework.

Share your passion for data and how it drives your problem-solving ethics. Discuss what aspects of the data science landscape you find most exciting—be it uncovering insights, improving systems, or contributing to meaningful projects.