Senior AI/ML Data Scientist (GCP/BigQuery)
Role details
Job location
Tech stack
Job description
We are seeking a highly experienced Senior AI/ML Data Scientist to join a critical project, driving machine learning systems that power personalization, customer intelligence, and strategic decision-making. Working at the intersection of classical ML, statistical modeling, and emerging Generative and Agentic AI, you will partner with cross-functional product and engineering teams to transform complex datasets into measurable business impact. The ideal candidate will have a strong foundation in quantitative research and extensive experience operationalizing models within the GCP ecosystem., * Design, develop, and deploy production-grade predictive ML systems, including propensity models and recommendation engines, from conception to production.
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Lead applied research and rigorous statistical analysis to inform model design, experimentation, and enterprise business strategy.
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Build, train, and operationalize scalable models on Google Cloud Platform using Vertex AI, BigQuery ML, Dataflow, and Cloud Run.
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Prototype and integrate cutting-edge Generative AI and Agentic AI capabilities into existing workflows and customer-facing products.
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Collaborate with data engineering teams to architect feature stores, robust training pipelines, and production model monitoring frameworks.
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Translate ambiguous business requirements into well-scoped technical solutions and present findings to diverse stakeholders.
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Mentor junior team members and champion best practices in modeling, MLOps, and experimentation.
Requirements
Primary (Must-Have):
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Expertise in Google Cloud Platform (GCP) (Vertex AI, BigQuery ML, Dataflow, Cloud Run)
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Strong Python programming skills (scikit-learn, TensorFlow/PyTorch, pandas, NumPy)
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Deep experience in propensity modeling, recommendation engines, and predictive ML systems
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Statistical analysis proficiency (causal inference, A/B testing, experimental design)
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Proven track record of deploying production-grade ML models (End-to-End lifecycle)
Secondary (Good to Have):
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Hands-on experience with Generative AI and Agentic AI (LLMs, RAG, prompt engineering)
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Experience designing feature stores and robust MLOps/model monitoring frameworks
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Strong communication skills to bridge technical research with non-technical business strategy
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Mentorship experience for junior data scientists and engineering teams
Benefits & conditions
Medical | Dental | Vision | 401(k)
EEOC Compliance
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment.