Riskified empowers merchants and shoppers to realize the full potential of eCommerce by making it safe, accessible, and frictionless. Our global team helps the world's most innovative eCommerce merchants eliminate risk and uncertainty from their business. Merchants integrate Riskified's machine learning platform to create trusted customer relationships, driving higher sales while reducing costs. Riskified has reviewed hundreds of millions of transactions and approved billions of dollars of revenue for global brands and fast-growing businesses across industries, including Wayfair, Wish, Peloton, Gucci, and many more. As of July 29th, 2021, Riskified has begun trading on NYSE under the ticker RSKD. Check out the Riskified Technology Blog for a deeper dive into our R&D work.
About the Role
As a Data Scientist in the Customer Trust group, you will play a pivotal role 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 line of products designed to detect and flag customers who exploit our merchants' return and refund policies for unjust financial gains. Your work will be instrumental in developing and implementing advanced techniques, including graph-based machine learning, to protect merchants from such abuses in real-time.
Our team applies cutting-edge techniques, including graph theory, classification models, NLP, and more, to extract maximum value from data in all shapes and sizes. As a Data Scientist, you will take charge of end-to-end project development and implementation. You'll need strong quantitative and analytical skills, a solid background in statistical modeling and machine learning, and a passion for problem-solving and data-driven decision-making. Your contributions will be crucial in fortifying our commitment to merchant protection and product excellence.
What You'll Be Doing
- Take ownership of end-to-end data science projects, from problem formulation to developing 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 extract maximum 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 drive business growth.
- Apply your expertise to break down complex projects into manageable, actionable parts, ensuring efficient project management and timely results.
- Conduct exploratory data analysis, model building, and evaluation, incorporating graph-based methodologies and classical 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 developing a robust machine learning system to support our new product offerings.
- Communicate complex findings and insights to both technical and non-technical stakeholders effectively.
- MSc in Statistics, Computer Science, Mathematics, or a related field.
- 3+ years of proven experience designing and implementing machine learning algorithms and techniques in a production-grade environment. Big advantage: Experience with big data tools like Spark and graph databases and applied graph theory.
- In-depth 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 experience in handling and analyzing large datasets efficiently, leveraging technologies such as Hadoop and Spark.
- A solid 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 background in machine learning 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 fast-growing and dynamic tech company with 750+ team members globally. We value collaboration and innovative thinking. We're looking for bright, driven, and passionate people to grow with us.
Our Tel-Aviv team is currently working in a hybrid of remote and in-office work for all our team members. We have recently moved to our new space in Tel Aviv - check it out here!
Some of our Tel Aviv Benefits & Perks:
- Equity for all employees, Keren Hishtalmut, pension
- Private medical insurance, extra 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
- Wide-ranging opportunities to volunteer and make an impact
- Commitment to your professional development with global onboarding, skills-based courses, full access to Udemy, lunch & learns
- Awesome Riskified gifts 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.