Data Scientist

As a Data Scientist at Riskified, your main responsibilities will include data exploration and preprocessing, statistical analysis, model development, and evaluation. You'll be expected to implement machine learning algorithms to solve business problems and improve processes. Collaborating with cross-functional teams, you will also be tasked with presenting complex results clearly to stakeholders, always aiming to drive data-driven decision-making.

To apply for the Data Scientist role at Riskified, you'll need a B.Sc in Computer Science, Mathematics, or Statistics, with an M.Sc being a plus. You should also have at least 3 years of proven experience with machine learning algorithms and their deployment in production environments. Proficiency in Python and experience with SQL is essential, along with strong analytical and critical thinking skills.

At Riskified, we utilize a wide array of technologies for our Data Science projects, including Python, SQL, and data manipulation tools such as Pandas and NumPy. Familiarity with machine learning libraries, data visualization tools, and cloud computing platforms will also be an advantage. Additionally, experience with tools like Airflow and Docker can help you excel in this role.

Riskified is committed to the professional development of its employees, including Data Scientists. We offer global onboarding, access to skills-based courses, and platforms like Udemy. Regular lunch & learns and team events also provide avenues for continuous learning and growth, ensuring that you stay up to date with the latest advancements in the Data Science field.

The work environment at Riskified is collaborative and dynamic. The Tel Aviv team operates in a hybrid model of remote and in-office work, allowing flexibility while fostering interaction between team members. The office is equipped with amenities and hosts various team-building events, making it a vibrant and engaging place to work.

When discussing a machine learning algorithm, clearly describe the problem you aimed to solve, the data you used, the algorithm you selected, and the reasoning behind your choice. Highlight the implementation steps and the impact of the results, using metrics to showcase improvements.

In your answer, emphasize the importance of data quality. Outline your steps: collecting data, identifying missing values, cleaning data, and transforming variables. Mention specific techniques or tools you prefer and how they ensure the integrity of your analysis.

Share your experience in extracting and creating features that enhance model performance. Discuss methods like one-hot encoding, normalization, and manual feature creation, elaborating on how they've improved your models in past projects.

Discuss different metrics relevant to your projects, such as accuracy, precision, recall, and F1 score. Explain how you utilized validation techniques like cross-validation and A/B testing to rigorously assess model performance before deployment.

Emphasize the importance of storytelling with data. Describe how you use data visualization tools to convey information and tailor your communication style to your audience, ensuring both technical and non-technical stakeholders understand your insights.

Narrate a specific situation where you identified a challenging problem, detailing the data analysis and modeling techniques you employed. Highlight the outcome, emphasizing the impact on your team's or company's decision-making processes.

Demonstrate your proficiency in SQL by mentioning specific queries you've written. Discuss how you've used SQL for data extraction, transformation, and analysis, and share any complex problems where SQL played a crucial role in your solutions.

Discuss your commitment to continuous learning through online courses, attending conferences, or participating in data science communities. Mention impactful resources you've found useful, such as research papers or influential podcasts in the field.

Share an example that illustrates your teamwork skills, highlighting how you effectively interacted with team members from diverse backgrounds, resolved conflicts, and contributed to a positive outcome for a data-driven project.

Your answer should reflect your alignment with Riskified’s mission and values. Express your passion for data science, your interest in the fraud prevention domain, and how the company’s culture and growth prospects excite you.