Job description

Job Description: Data Scientist – Google Cloud & Vertex AI 9+ Years of experience required Build, deploy, and scale production-grade machine learning and generative AI solutions on Google Cloud Looking for a hands-on Data Scientist with deep expertise in Google Cloud Platform (GCP) and the Vertex AI ecosystem to design, build, and operationalize machine learning and generative AI solutions that deliver measurable business value. In this role, you will work closely with business stakeholders, data engineers, and ML engineers to turn complex business problems into scalable, production-ready models, covering the full lifecycle from data exploration and feature engineering to model deployment, monitoring, and continuous improvement on Vertex AI. Key Responsibilities Data Science & Modeling • Partner with business stakeholders to frame problems, define success metrics, and translate requirements into analytical and ML solutions. • Perform exploratory data analysis, statistical modeling, and hypothesis testing on large, complex datasets in BigQuery. • Develop, train, and tune supervised and unsupervised models for use cases such as demand forecasting, customer segmentation, churn prediction, recommendation, pricing, and anomaly detection. • Engineer and manage reusable features using Vertex AI Feature Store and BigQuery. • Use Vertex AI AutoML and BigQuery ML for rapid prototyping, and custom training (TensorFlow, PyTorch, scikit-learn, XGBoost) for advanced use cases. • Run hyperparameter tuning and track experiments, parameters, and metrics using Vertex AI Training and Vertex AI Experiments. Generative AI & LLM Solutions • Design and build generative AI applications using Gemini models and foundation models from Vertex AI Model Garden. • Implement retrieval-augmented generation (RAG) solutions using Vertex AI Vector Search, embeddings APIs, and grounding with enterprise data. • Apply prompt engineering, prompt evaluation, and model tuning techniques (for example, supervised fine-tuning) using Vertex AI Studio. • Build conversational and agentic AI solutions using Vertex AI Agent Builder and related Google Cloud services. • Evaluate generative AI outputs for quality, accuracy, safety, and bias using Vertex AI evaluation tools and responsible AI practices. MLOps, Deployment & Monitoring • Build end-to-end, reproducible ML workflows using Vertex AI Pipelines (Kubeflow Pipelines / TFX). • Version, govern, and promote models using Vertex AI Model Registry. • Deploy models for online and batch prediction through Vertex AI Endpoints and Batch Prediction, optimizing for latency, scale, and cost. • Configure Vertex AI Model Monitoring to detect training-serving skew, data drift, and performance degradation. • Implement CI/CD for ML using Cloud Build, Artifact Registry, and Git-based workflows. • Apply Explainable AI (Vertex Explainable AI) to provide transparency into model predictions for business users. Collaboration & Communication • Work with data engineers to design scalable data pipelines using Dataflow, Dataproc, Pub/Sub, and Cloud Composer. • Present insights, model results, and recommendations to technical and executive audiences using clear storytelling and visualizations (for example, Looker or Looker Studio). • Mentor junior data scientists and promote best practices in coding, experimentation, and model governance. • Stay current with the Google Cloud AI roadmap and recommend new capabilities that can accelerate business outcomes. Google Cloud & Vertex AI Technical Expertise Area Tools & Services Vertex AI – Core ML Vertex AI Workbench, AutoML, Custom Training, Hyperparameter Tuning, Experiments, Feature Store Vertex AI – MLOps Vertex AI Pipelines, Model Registry, Endpoints, Batch Prediction, Model Monitoring, Explainable AI, ML Metadata Vertex AI – Generative AI Gemini models, Model Garden, Vertex AI Studio, Vector Search, Embeddings APIs, Agent Builder, Gen AI evaluation Data & Analytics BigQuery, BigQuery ML, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Cloud Storage, Dataplex Deployment & DevOps Cloud Run, Google Kubernetes Engine (GKE), Cloud Build, Artifact Registry, Cloud Functions, Terraform Visualization Looker, Looker Studio Security & Governance IAM, VPC Service Controls, Cloud Logging and Monitoring, Data Catalog / Dataplex Required Qualifications • Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field. • 4+ years of hands-on experience in data science or machine learning, including at least 2 years building and deploying models on Google Cloud Platform. • Proven experience with Vertex AI for model training, deployment, and pipeline orchestration in production environments. • Strong programming skills in Python (pandas, NumPy, scikit-learn) and advanced SQL, especially in BigQuery. • Practical experience with at least one deep learning framework such as TensorFlow, Keras, or PyTorch. • Solid foundation in statistics, probability, experimental design (A/B testing), and machine learning algorithms. • Experience with version control (Git), containerization (Docker), and CI/CD practices for ML. • Ability to communicate complex technical concepts clearly to non-technical stakeholders.

Requirements

Must-haves

Job Description: Data Scientist – Google Cloud & Vertex AI
9+ Years of experience required
Build, deploy, and scale production-grade machine learning and generative AI solutions on Google Cloud
Data Science & Modeling
Partner with business stakeholders to frame problems, define success metrics, and translate requirements into analytical and ML solutions.
Perform exploratory data analysis, statistical modeling, and hypothesis testing on large, complex datasets in BigQuery.
Develop, train, and tune supervised and unsupervised models for use cases such as demand forecasting, customer segmentation, churn prediction, recommendation, pricing, and anomaly detection.
Engineer and manage reusable features using Vertex AI Feature Store and BigQuery.
Use Vertex AI AutoML and BigQuery ML for rapid prototyping, and custom training (TensorFlow, PyTorch, scikit-learn, XGBoost) for advanced use cases.
Run hyperparameter tuning and track experiments, parameters, and metrics using Vertex AI Training and Vertex AI Experiments.
Generative AI & LLM Solutions
Design and build generative AI applications using Gemini models and foundation models from Vertex AI Model Garden.
Implement retrieval-augmented generation (RAG) solutions using Vertex AI Vector Search, embeddings APIs, and grounding with enterprise data.
Apply prompt engineering, prompt evaluation, and model tuning techniques (for example, supervised fine-tuning) using Vertex AI Studio.
Build conversational and agentic AI solutions using Vertex AI Agent Builder and related Google Cloud services.
Evaluate generative AI outputs for quality, accuracy, safety, and bias using Vertex AI evaluation tools and responsible AI practices.
MLOps, Deployment & Monitoring
Build end-to-end, reproducible ML workflows using Vertex AI Pipelines (Kubeflow Pipelines / TFX).
Version, govern, and promote models using Vertex AI Model Registry.
Deploy models for online and batch prediction through Vertex AI Endpoints and Batch Prediction, optimizing for latency, scale, and cost.
Configure Vertex AI Model Monitoring to detect training-serving skew, data drift, and performance degradation.
Implement CI/CD for ML using Cloud Build, Artifact Registry, and Git-based workflows.
Apply Explainable AI (Vertex Explainable AI) to provide transparency into model predictions for business users.
Collaboration & Communication

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimachine learninggcpmlllmci/cdkubernetespythonsqldocker

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Effort: medium