Chewy is seeking a highly motivated, goal-oriented, and analytical, Data Scientist to join our exciting and fast paced company in Boston, MA. Our team is growing and if you are equally passionate about data analysis, e-commerce, and career growth, an opportunity at Chewy may be a great match! You will be focused on solving business problems by developing and implementing best-in-class fraud prevention and detection models. In this role, you will be the analytical expert for identifying and retooling suitable machine learning algorithms that can enhance the fraud risk ranking of particular transactions and/or applications for new products. You will cooperate with cross-functional teams across the organization in finding insights and developing the right data solutions.
You should be effective at organizing large amounts of data from multiple data sources and build predictive anomaly detection machine learning models that will help in identifying valid Fraudulent transactions. Your expertise and insights will help us effectively utilize data assets like past Order/Fraud history to control fraud loss and improve customer experience.
What youll do:
- Research, design, and implement predictive models, use data sciences to detect and prevent high risk fraud and reduce false positives.
- When that means high precision classification, you will work fraud and trust SMEs to understand domain knowledge, business expectation and turn them into data driven machine learning approach.
- Develop machine learning, data mining, statistical, and graph-based algorithms designed to analyze massive data sets; partner with Cloud technologists to ensure proper implementation and usage of said algorithms
- Analyze large data sets to develop multiple, custom models and algorithms to drive innovative correlations for fraud and acceptance data as well as social data
- Operate at depth - Ideate, brainstorm and drive projects from conception to completion
Conduct exploratory data analysis, supervised, unsupervised and semi-supervised machine learning to identify fraud trend, segment and clusters, and optimization opportunity
- Lead identification of trends and KPIs with the objective of improving customer performance
- Mentor junior data analysts and demonstrate Data Science and development best practices
What youll need
- An M.S., P.H.D., or equivalent experience in a related discipline (Engineering, Mathematics, Physics, Finance or C.S.)
- Experience in Python, R, or other Data Science object-oriented programming language
- Experience with applying machine learning techniques at scale and their tuning parameters (Neural Networks, Random Forest, Bayesian Models, K-Means Clustering)
- 5 years of experience applying Machine Learning, Statistical Modeling, and Data Mining.
- Experience in anomaly detection algorithms including Regressions, Random Forrest, GBMs, Support Vector Machines, KNNs, Neural Nets and Social Network Link analysis.
- Experience with SQL, NoSQL, and unstructured databases (MySQL, Hadoop, Redshift, MongoDB)
- Applied data science techniques, data management and advanced analytic techniques while doing fraud analysis/anomaly detection.
- Hands on experience with software design for scalability, reliability, and performance
- Proven track record in developing mathematical models to predict business outcomes and determine cause and effect relationships
- Candidate must be organized, driven, and exhibit critical thinking skills.
- Experience working in Agile teams (Scrum, Kanban)
- Experience in working in eCommerce business.
- Knowledge of / experience with multi-channel and cross-channel attribution.
- Extensive CLI and UNIX experience we live in the terminal!