Senior Data Scientist

Are you ready to shape the personalized shopping experiences of millions of customers? Our Relevancy Team is responsible for architecting, scaling, and optimizing highly personalized algorithmic experiences for a digital platform ranking among the Top 10 E-commerce companies in the United States.

We operate at a massive data scale, delivering trillions of automated recommendations across our web and mobile applications. Our diverse data science portfolio spans product and coupon recommender systems, intelligent substitute logic, and interactive shoppable recipes. We are seeking a talented, execution-focused Senior Data Scientist with a deep specialization in search and recommendation architectures to push the boundaries of digital personalization.

The Ideal Candidate: A proactive builder who possesses a proven track record of developing deep learning models at scale, possesses extreme fluency with major ML frameworks (TensorFlow/PyTorch), and understands the practical nuances of real-time multi-objective evaluation pipelines.

Key Responsibilities

• Model Development & Innovation: Design, develop, and implement end-to-end deep learning recommender systems engineered specifically for complex, high-frequency retail and digital e-commerce architectures.
• Advanced Personalization: Build and tune predictive models that map customer preferences, dietary frameworks, historical transactions, and real-time behavioral signals into tailored product, coupon, and substitute recommendations.
• Algorithmic Diversity: Formulate and deploy sophisticated recommendation diversity strategies that safely expose users to broader product catalogs without degrading baseline conversion metrics.
• Rigorous Experimentation: Establish robust offline evaluation frameworks, interpretability guardrails, and execute high-velocity live A/B testing to benchmark algorithmic iterations.
• Cross-Functional Production MLOps: Partner seamlessly with ML Engineers and Data Engineers to integrate disparate raw data streams and facilitate scalable model deployment, automated versioning, and low-latency serving.
• Knowledge Amplification: Build, scale, and maintain shared internal tools, analytics tracking dashboards, and modular code libraries to champion technical knowledge-sharing across the broader data science discipline.

Requirements & Technical Qualifications

• Core Experience: 2 years of verified industry experience designing, building, and deploying deep learning algorithms explicitly within large-scale recommender or search systems.
• Deep Learning Mastery: High proficiency and production experience utilizing TensorFlow or PyTorch.
• Big Data Ecosystems: Expert-level command of SQL, Python, and Spark for data manipulation. Hands-on experience within a Databricks environment is highly advantageous.
• Statistical Depth: Deep mastery of statistical methodologies, exploratory data analysis (EDA), and design of experiments (A/B testing).
• Cloud Infrastructure: Proven familiarity navigating cloud-native data architectures (specifically GCP or Azure).
• Industry Domain: Prior data science experience in the retail, grocery, or digital e-commerce verticals is a significant plus.
• Interpersonal Excellence: Exceptional verbal and written communication skills with a demonstrated ability to present sophisticated technical architectures plainly to both technical and non-technical business stakeholders.