Sr Data Scientist

Description

• Manage timelines and expectations transparently with both the data science team and business stakeholders.
• Proactively gather feedback from stakeholders and adapt solutions for adoption and measurable impact.
• Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore to solve business problems in pricing, sales, operations, and customer interactions.
• Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies.
• Integrate agents with enterprise systems and protocols (MCP servers, A2A protocol, internal APIs).
• Design and run experiments (A/B tests, multi-armed bandits, uplift models) to measure and optimize model and agent performance.
• Design, build, and deploy pricing recommendation models to optimize sales velocity, revenue, and division-level targets.
• Drive experimentation with new tools and approaches, ensuring robustness, governance, and scalability in production deployments.
• Share learnings with the broader team to raise the bar on data science and agentic development across the organization.
• Build personalization algorithms for tailored product recommendations and communications across email, text, and digital platforms.
• Develop sales forecasting and demand prediction models to support pricing and inventory decisions.
• Ensure observability and reliability of deployed agents, including logging, evaluation, monitoring, and drift detection.
• Translate complex data science and statistical concepts into clear recommendations, stories, and visualizations for executives and non-technical audiences.
• Engineer and maintain data pipelines and systems supporting all models and agents, ensuring scalability and reliability.

Requirements

• Hands-on experience with AI development frameworks (LangChain, Strands, Amazon Bedrock, AgentCore, or equivalent).
• Exposure to machine vision techniques (CNNs, transfer learning, embeddings) and NLP techniques (embeddings, transformers, prompt engineering).
• Comfort working with large-scale, imperfect real-world datasets and making progress despite complexity.
• Strong engineering skills: ability to design and maintain production pipelines, microservices, and scalable systems.
• Proven ability to navigate ambiguity, rapidly prototype, and move solutions into production.
• Experience with system integrations: APIs, A2A protocol, MCP servers, orchestration pipelines.
• Bonus: experience with RAG pipelines, LLM fine-tuning, RLHF, multi-agent orchestration, feature stores, survival analysis/churn modeling, and attribution modeling.
• Strong proficiency in Python and SQL, with experience owning the full data science stack (data pipelines + models + deployment).
• Experience building and deploying large-scale recommender systems (collaborative filtering, embeddings, contextual bandits).
• Experience with pricing optimization, revenue management, economic modeling, and price elasticity/demand modeling.
• Experience with experimentation frameworks (A/B testing, uplift modeling, multi-armed bandits, causal ML).
• 5+ years of relevant experience (1+ with PhD, 3+ with MS) as a data scientist, ML engineer, or applied AI developer delivering production-ready models and systems.
• Collaborative communicator who can align technical solutions with business priorities across diverse stakeholders.
• Understanding of AI agent observability (evaluation frameworks like LangFuse, RAGAS, Weights & Biases, custom monitoring).
• Bachelor’s or Master’s degree in Statistics, Economics, Math, Computer Science, Data Science, Machine Learning, or related field (or equivalent experience).
• Familiarity with real-time or near-real-time systems (Kafka, Kinesis, Flink, or similar) for scalable personalization.
• Bonus: background in real estate analytics, revenue management systems, or retail pricing optimization.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance