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Job Information, Inc Senior/ Lead Data Scientist, Commerce Cloud Einstein in Cambridge, Massachusetts

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Products and Technology

Job Details

Salesforce Commerce Cloud is the global leader of Cloud based (SaaS) E-Commerce solutions that enable the world’s premier retailers to maintain a robust online shopping presence. We empower our clients to improve their ROI by giving them the tools to effortlessly design and deploy seamless E-Commerce sites across traditional web, mobile, tablet and in-store applications. Commerce Cloud provides a highly scalable, integrated cloud platform that allows our clients to rapidly launch and manage multiple e-commerce stores, initiate unique marketing campaigns, and drive customer traffic across a global footprint. Our model serves for an environment of constant innovation which extends to our technology and internal company culture. Come join our a platform serving 3000+ sites with more than one billion unique visitors.

We are looking for a top-notch data scientist to join the Commerce Cloud Einstein team at our office in Cambridge, Massachusetts - remote initially. The Einstein product suite allows retailers and e-commerce companies to leverage real-time shopper data from across the globe to personalize customer interactions across the ecommerce ecosystem. We do this by capturing data from every customer touch point, mining behavioral signals using machine learning and advanced predictive algorithms and overlaying deep retail domain knowledge. Commerce Cloud Einstein technology enables merchandisers and marketers to drive substantial engagement and revenue.

The successful candidate will work with world-class database gurus, software engineers and data scientists on expanding the personalization and machine learning capabilities of the Einstein platform. If you have a passion for machine learning and experience with personalization and recommender systems, and a demonstrated history of delivering results, then we are very interested in talking to you.


  • Contribute to the research, design, and construction of cutting-edge personalization systems that touch hundreds of millions of shoppers annually

  • Apply state of the art machine learning techniques (e.g., deep learning and reinforcement learning) to product, shopper and business-user data to take recommender systems and contextual personalization to the next level, as well as to devise new data products for use by digital commerce teams.

  • Rapidly iterate and develop algorithmic/model-based approaches, based on machine learning techniques, to retail prediction/targeting problems

  • Work closely with Engineering team to "productionize" winning approaches


Must-Have :

  • Practical experience in machine learning or personalization

  • MS or PhD in a quantitative discipline with 2+ years of experience or a BS in a quantitative discipline with 5+ years of experience

  • Fluent in building/prototyping machine learning models and algorithms and wrangling large datasets

  • Knowledgeable about standard machine learning approaches (Regression, Cross-Validation, Boosting, Matrix-Factorization, Decisions Trees, Clustering, CNNs, RNNs, Transformers, GANs)

  • Proficient in using Python (e.g., Numpy, Pandas, PyTorch, SciPy, scikit-learn, JAX) to implement machine learning models and algorithms

  • Proficient in SQL, shell scripting and Unix/Linux command-line tools


  • Demonstrated experience with actually shipping code, getting data science into production

  • AWS experience

  • Some low level programming experience (C/ C++/ Fortran/ Java/ Go/ Julia/ Rust, etc.)

  • Have build and trained various deep learning algorithm from scratch, including for reinforcement learning

  • Can explain how inverse propensity scoring helps offline training and how to handle the increased variance

  • Comfortable using profiling tools to make algorithm more scalable or faster

  • Achieved a top rank in machine learning competitions like Kaggle or KDD Cup

  • Presented a paper at RecSys – ACM Recommender Systems (or similar conferences)

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