Data Scientist, Risk & Fraud

About Super.com

We started Super.com to help maximize lives–both the lives of our customers and the lives of our employees– so that everyone can experience all that life has to offer. For our employees, our promise is that Super.com">Super.com is more than just a job; it’s an opportunity to unlock one’s potential, where learning is celebrated and impact is realized. We are more than a fast-paced, high-growth tech company; we care about our people and take career progression seriously. This is your career and our aim is to supercharge it through the people, the work, and the programs that fuel who we are.

About this Team

This role is part of our newly formed Risk Fraud Automation team, where evangelizing and building a strong culture around risk and fraud management will be a crucial part of our mission. Being Data Driven is a core value of Super.com">Super.com and this team plays a huge part in this. We are long time advocates and users of Snowflake, DBT Amplitude. This isn’t a place where we are convincing the business the value of data, or just using these tools for the first time. We have maturity in our technical practices, launch a new experiment every day, and are looking for teammates to roll up their sleeves and do high quality data work.

About this Role

Our data scientists are embedded team members, and work alongside PM’s, software engineers and designers in our mission aligned team (MAT) model. This allows our team members to gain deep experience and context into specific business problems and verticals to grow quickly to become subject matter experts. This position offers a dynamic and impactful opportunity to assume ownership of your work as you conduct in-depth analyses and productionize your work.

Responsibilites

• Apply statistical analysis and machine learning techniques to uncover insights, build predictive models, and support data-driven decision-making
• Investigate data trends, patterns, and anomalies; assist in diagnosing issues and identifying opportunities through exploratory data analysis (EDA)
• Access, clean, and transform large, raw datasets to prepare them for modeling and analysis
• Collaborate closely with product, engineering, and business teams to translate problems into data science solutions
• Autonomously identify business challenges that have impactful data solutions and build the right solution to the problem
• Design and execute personalization experiments (e.g., A/B tests) to test hypotheses, measure product changes, and inform business strategies
• Develop, prototype, and validate machine learning models and algorithms
• Create clear visualizations and reports to communicate technical findings and model results to stakeholders
• Follow best practices for reproducibility, model evaluation, documentation, and version control in data science workflows
• Support efforts to improve data quality, feature engineering processes, and model monitoring practices

Preferred Experience

• 2+ years of experience in data science roles, preferably within risk fraud related roles
• Experience with web analytics tools (GA4, Amplitude, Mixpanel, etc)
• A technical degree in data science, computer science, analytics, or AI with a proven ability to autonomously build and evaluate predictive models
• Intermediate knowledge of SQL with the ability to create clean and transform datasets, ideally with DBT
• Collaborative and verbose communication about why and how we approach business questions
• Strong python programming skills with working knowledge of machine learning packages such as sklearn, pandas, numpy, tensorflow, etc.

We Believe in Equal Opportunity

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.Accommodations are available on request for candidates taking part in all aspects of the selection process. If needed, please notify our Talent Acquisition Partner.

Originally posted on Himalayas

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