Data Science L1

As a Data Science L1 at our company, your primary responsibilities include data analysis and modeling, model development and validation, and credit risk assessment. You will leverage your analytical skills to identify trends in large datasets, develop classification models, and evaluate credit risk based on those models. You'll also be engaged in feature engineering, cross-functional collaboration, and ongoing monitoring of model performance.

To qualify for the Data Science L1 position at our company, you should have a background in Data Science, Statistics, or Computer Science, along with programming knowledge. A minimum of 1-2 years of experience in data science or analytics is required, as well as proficiency in Python and SQL. Familiarity with data engineering and AWS services is a plus but not mandatory.

The Data Science L1 role is crucial for credit risk assessment as you will be responsible for evaluating potential borrowers using data-driven models. This involves assigning credit scores and probability of default values based on insights derived from extensive data analysis. Your creative problem-solving skills will allow you to incorporate alternative data into the assessment process, enhancing the accuracy of risk evaluation.

Key skills for success as a Data Science L1 include strong analytical abilities, expertise in Python and SQL, and familiarity with machine learning concepts. Moreover, creativity in feature engineering and a collaborative mindset to work cross-functionally will greatly enhance your contributions. Staying updated with the latest advancements in data science will also be essential.

In the Data Science L1 role at our company, you will experience ample opportunities for growth and innovation. You will be encouraged to stay abreast of new technologies and data methodologies. Additionally, your contributions towards research and the exploration of new approaches in credit risk assessment will not only benefit our models but also ensure your continuous professional development in the field.

When explaining your experience with classification models, detail the data preprocessing steps, feature selection, algorithm used, and how you evaluated the model’s performance. Mention any metrics you focused on and how you refined the model based on feedback.

Discuss your systematic approach to data cleaning, which should include handling missing values, identifying outliers, and ensuring consistency across datasets. Providing specific examples will highlight your experience effectively.

Share an experience where you worked with team members from different departments. Explain the project's objective, your role, and how you facilitated communication and collaboration to achieve project goals.

Outline techniques you consider for feature engineering, such as transformations, interaction features, and domain-related features. Discuss how they impact model performance and your approach to testing their effectiveness.

Illustrate your experience by describing specific metrics you’ve tracked post-deployment, methods for assessing model drift, and steps taken to optimize or retrain models to maintain accuracy against changing data.

Provide a detailed example of a project where alternative data sources significantly influenced your analysis or model development. Discuss why you chose that data and any hurdles you faced in integrating it.

Share the resources you utilize, such as academic journals, data science blogs, online courses, and conferences. Emphasize your commitment to continuous learning and professional development in the field.

Mention your proficiency in Python and SQL. Give examples of specific tasks you've accomplished using these languages, such as developing models, performing data analysis, or querying databases for insights.

Recount a specific challenge, whether technical or process-related, and describe the steps you took to address it. This will demonstrate your problem-solving skills and ability to adapt in the face of obstacles.

Convey your enthusiasm for the role by discussing aspects of the job description that resonate with you, particularly around credit risk assessment or the importance of creativity in data solutions. Express how your skills align with the company's goals.