Senior, Data Scientist

About the Role

Key Responsibilities

• Design, develop, and productionalize machine learning models for personalized recommendations, layout optimization, and content generation (visual and textual) using structured and unstructured data.
• Lead the development of large-scale ML models leveraging deep learning (Transformers, multi-modal, LLMs), reinforcement learning, and personalization frameworks (e.g., agentic systems).
• Collaborate cross-functionally with engineering, product, and design teams to integrate ML systems into customer-facing applications.
• Build scalable data pipelines and feature stores with data engineers for real-time and batch personalization use cases.
• Employ and experiment with pre-trained foundation models (e.g., BERT, RoBERTa, T5, CLIP) for customer representation and product understanding.
• Apply best-in-class practices in model validation, monitoring, explainability (e.g., SHAP, counterfactuals), and lifecycle management.
• Contribute to research and innovation in personalization by exploring novel architectures and publishing internally or externally where applicable.

Requirements

• Have a MS/PhD in relevant technical degree (preferably Computer Science, Machine Learning, Operations Research, Applied Mathematics, Statistics, Engineering etc.) with 2-4 years of relevant work experience.
• Proven experience developing and deploying ML models at scale in a production environment, especially for recommendation systems, ranking, or NLP tasks.
• Proficiency in Python, SQL, and PySpark. Strong familiarity with ML libraries such as TensorFlow, PyTorch, Scikit-Learn, XGBoost.
• Strong knowledge of ML evaluation and diagnostic techniques (A/B testing, ROC-AUC, Precision@K, RMSE, etc.).
• Experience with foundation models and LLMs.
• Background in reinforcement learning or agent-based personalization frameworks.
• Experience working with big data tools (Hive, Spark) and modern MLOps practices.

Nice to Have

• Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics.
• Successful completion of one or more assessments in Python, Spark, Scala, or R.
• Using open source frameworks (for example, scikit-learn, TensorFlow, Torch).
• Knowledge of creating inclusive digital experiences and implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Qualifications

• Option 1: Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 years of experience in an analytics related field.
• Option 2: Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 year of experience in an analytics related field.
• Option 3: 5 years of experience in an analytics or related field.

Benefits & Perks

• Incentive awards for performance.
• 401(k) match, stock purchase plan.
• Paid maternity and parental leave.
• Paid time off (PTO), including sick leave, holidays, and other purposes.
• Multiple health plans.
• Health benefits including medical, vision, and dental coverage.
• Health and wellness programs such as Live Better U education benefit.
• Company discounts, life insurance, disability benefits, military leave pay, adoption and surrogacy expense reimbursement, and more.