Data Scientist

About Us

Riskified empowers businesses to unleash ecommerce growth by taking risk off the table. Many of the world's biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists and researchers, Riskified's AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights. Riskified is proud to work with incredible companies in virtually all industries including Booking.com, Acer, Gucci, Lorna Jane, GoPro, and many more.

We thrive in a collaborative work setting, alongside great people, to build and enhance products that matter. Abundant opportunities to create and contribute provide us with a sense of purpose that extends beyond ourselves, leaving a lasting impact. These sentiments capture why we choose Riskified every day.

About the Role

The Data Science department plays a pivotal role in our company, generating value to Riskified by developing algorithms and analytical production-grade solutions. We leverage advanced techniques and algorithms to provide maximum value from data in all shapes and sizes (such as classification models, NLP, anomaly detection, graph theory, deep learning, and more). As a Data Scientist, you will assume the classic data-science role of an end-to-end project development and implementation practitioner. Being part of the team requires a mix of hard quantitative and analytical skills, solid background in statistical modeling and machine learning, a technical data-savvy nature, along with a passion for problem-solving and a desire to drive data-driven decision-making.

What You'll Be Doing
Data Exploration and Preprocessing: Collect, clean, and transform large, complex data sets from various sources to ensure data quality and integrity for analysis
Statistical Analysis and Modeling: Apply statistical methods and mathematical models to identify patterns, trends, and relationships in data sets, and develop predictive models
Machine Learning: Develop and implement machine learning algorithms, such as classification, regression, clustering, and deep learning, to solve business problems and improve processes
Feature Engineering: Extract relevant features from structured and unstructured data sources, and design and engineer new features to enhance model performance
Model Development and Evaluation: Build, train, and optimize machine learning models using state-of-the-art techniques, and evaluate model performance using appropriate metrics
Data Visualization: Present complex analysis results in a clear and concise manner using data visualization techniques, and communicate insights to stakeholders effectively
Collaborative Problem-Solving: Collaborate with cross-functional teams, including product managers, data engineers, software developers, and business stakeholders to identify data-driven solutions and implement them in production environments
Research and Innovation: Stay up to date with the latest advancements in data science, machine learning, and related fields, and proactively explore new approaches to enhance the company's analytical capabilities
Qualifications
B.Sc (M.Sc is a plus) in Computer Science, Mathematics, Statistics, or a related field
3+ years of proven experience designing and implementing machine learning algorithms and successfully deploying them to production.
Strong understanding and practical experience with various machine learning algorithms.
Proficiency in Python, Experience with SQL and data manipulation tools (e.g., Pandas, NumPy) to extract, clean, and transform data for analysis
Solid foundation in statistical concepts and techniques, including hypothesis testing, regression analysis, time series analysis, and experimental design
Strong analytical and critical thinking skills to approach business problems, formulate hypotheses, and translate them into actionable solutions
Proficiency in data visualization libraries, to create meaningful visual representations of complex data
Excellent written and verbal communication skills to present complex findings and technical concepts to both technical and non-technical stakeholders
Demonstrated ability to work effectively in cross-functional teams, collaborate with colleagues, and contribute to a positive work environment

Advantages:

Experience in the fraud domain
Experience with Airflow, CircleCI, PySpark, Docker and K8S
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. 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!
In the News

Geektime: Riskified Goes Public

Walla!: Happy Hour at the Riskified Offices

Geektime Insider: A look at Riskified Tel Aviv

Globes: Riskified to contribute the highest amount up to date to Tmura

Globes: Riskified is among Israel's fastest growing companies

TechCrunch: Riskified Prevents Fraud on Your Favorite E-commerce Site

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.