Data Scientist

At SWAG Live's data team, our mission is to democratize data by building a self-service data platform. We aim to empower internal teams to access, interpret, and derive valuable insights from data effectively.

As a Data Scientist in this role, you will collaborate with a multidisciplinary team of engineers and analysts to tackle diverse challenges using quantitative techniques such as statistical analysis and machine learning. You will work with large, complex event-based datasets, conduct exploratory data analysis (EDA), define requirements, and develop & deploy models.

We are particularly seeking a data scientist with experience in building customized recommendation models and a strong interest in product-focused machine learning development.

Responsibilities

1. Leverage state-of-the-art algorithms to build fully customized recommenders and other growth models.
2. Design, deploy and maintain all components necessary for modeling, including feature engineering, automatic model training & tuning and engineering toolchains.
3. Create a comprehensive monitoring framework to evaluate model performance and provide actionable insights to drive business growth, focusing on awareness conversion and transactions.
4. Understand stakeholder business requirements and design end-to-end machine learning/AI solutions that are effective, practical, and robust in addressing business challenges.
5. Collaborate closely with data engineers and backend engineers to develop scalable systems.
6. Communicate efficiently with cross-functional teams, promote the implementation of strategic applications, and drive continuous optimization.

Requirements

1. 3+ years in at least one of the following areas: search, recommendation systems, ads, content understanding/moderation, or anti-fraud.
2. Solid experiences in Python and related Data Analysis libraries (e.g., pandas, numpy, matplotlib, scikit-learn).
3. Proficient in at least one Deep Learning framework (e.g., TensorFlow, PyTorch, Keras, Theano).
4. Familiarity with Big Data tools such as Ray, Hive SQL, Spark, or MapReduce.
5. Strong understanding of the software development lifecycle and low-level code optimization.
6. Solid theoretical foundation in commonly used algorithms and strong statistical intuition.
7. Excellent communication and collaboration skills; ability to explore new technologies with the team and drive technical innovation.
8. Highly curious, self-motivated, and eager to take on challenges and strive for excellence.

Good to have

1. Prior experience in large-scale recommendation systems or search engines.
2. Familiarity with deep learning-based recommendation models such as DLRM, BERT4Rec, and NCF.
3. Experience with API service development and deployment.
4. Experience working with GCP and containerization technologies.
5. Expertise in building LLM applications and working with foundational models (e.g., LLaMA, GPT) and frameworks like LangChain and Weights & Biases.