Data Scientist

About Us

Riskified allows merchants and consumers to unlock the full potential of eCommerce by making it secure, accessible, and effortless. Our global team assists the world's most innovative eCommerce businesses in eliminating risk and unpredictability. Through integrating Riskified's machine learning platform, merchants build trusted customer relationships, driving increased sales while reducing costs. Riskified has examined hundreds of millions of transactions and approved billions of dollars of revenue for global brands and rapidly growing companies across industries, including Wayfair, Wish, Peloton, Gucci, and many. As of July 29th, 2021, Riskified began trading on the NYSE under the ticker RSKD. Check out the for a deeper understanding of our R&D work.

About the Role

As a Data Scientist in the Customer Trust group, you will play an essential part in delivering value by developing production-grade analytical solutions and collaboratively working with cross-functional teams, including Product and Engineering. In this role, you will focus on a new range of products designed to detect and mark customers who exploit our merchants' return and refund policies for unjust financial gains. Your work will be crucial in creating and implementing advanced techniques, including graph-based machine learning, to protect merchants from such abuses in real-time.

Our team uses cutting-edge techniques, such as graph theory, classification models, NLP, and more, to maximize data value in all forms and sizes. As a Data Scientist, you will manage end-to-end project development and implementation. You'll need to possess strong quantitative and analytical skills, a firm foundation in statistical modeling and machine learning, and a passion for problem-solving and data-driven decision-making. Your contributions will be vital in strengthening our commitment to merchant protection and product excellence.

What You'll Be Doing

• Take responsibility for end-to-end data science projects, from problem formation to the development of production-grade analytical solutions.
• Collaborate with cross-functional teams to ensure successful project breakdown and timely delivery.
• Develop and implement advanced techniques and algorithms, including graph-based machine learning, alongside other methods, such as classification models, semi-supervised learning, anomaly detection, and more, to maximize value from various data sources.
• Collaborate with business stakeholders, including merchants, to understand their needs and objectives and translate them into innovative data science solutions that promote business growth.
• Apply your expertise to break down complex projects into manageable, actionable components, ensuring efficient project management and timely results.
• Conduct exploratory data analysis, model building, and evaluation, integrating graph-based methodologies and traditional machine learning to uncover insights and patterns.
• Continuously explore and evaluate new technologies and tools to stay at the forefront of data science, contributing to the development of a robust machine learning system to support our new product offerings.
• Communicate complex findings and insights effectively to both technical and non-technical stakeholders.

Qualifications

• Masters in Statistics, Computer Science, Mathematics, or a related field.
• 3+ years of proven experience in designing and implementing machine learning algorithms and techniques in a production-grade environment. A significant advantage: Experience with big data tools like Spark and graph databases and applied graph theory.
• Deep understanding and practical experience with various machine learning algorithms, including graph-based approaches, such as network analysis and related techniques, as well as classical machine learning methods.
• Proficiency in programming languages such as Python or Pyspark for data manipulation, statistical analysis, and machine learning model development.
• Experience in Big Data Analytics techniques and tools, with the ability to handle and analyze large datasets efficiently, leveraging technologies such as Hadoop and Spark.
• A sturdy foundation in statistical concepts and techniques, like statistical inference, probability, and experimental design.
• Strong analytical and critical thinking skills, enabling you to approach complex business problems, formulate hypotheses, and translate them into actionable solutions across various data science domains.
• Excellent written and verbal communication skills for presenting complex findings and technical concepts to various stakeholders.
• Demonstrated ability to work effectively in cross-functional teams, collaborate with colleagues, and contribute to a positive work environment

If you are a passionate Data Scientist with a strong machine learning background and a desire to make a significant impact with your analytical skills, we would love to hear from you. Join our team and be a part of driving data-driven decision-making at Riskified.

Life at Riskified

We are a rapidly growing and dynamic tech company with over 750 team members worldwide. We value collaboration and innovative thinking. We're looking for bright, driven, and passionate people to grow with us.

Our Tel-Aviv team currently works in a mix of remote and in-office settings for all team members. We've recently relocated to our new space in Tel Aviv - check it out!

Some of our Tel Aviv Benefits & Perks:

• Equity for all employees, Keren Hishtalmut, pension
• Private medical insurance, additional time off for parents and caregivers
• Commuter and parking benefits
• Team events, fully-stocked kitchen, lunch stipend, happy hours, yoga, pilates, functional training, basketball, soccer
• Diverse opportunities to volunteer and make a difference
• Commitment to your professional development with global onboarding, skills-based courses, full access to Udemy, lunch & learns
• Awesome Riskified merchandise and swag!

Riskified is deeply committed to the principle of equal opportunity for all individuals. We do not discriminate based on race, color, religion, sex, sexual orientation, national origin, age, disability, veteran status, or any other status protected by law.