Data Scientist

  • Alpharetta, GA
  • Posted 1 day ago | Updated 1 day ago

Overview

Remote
On Site
Hybrid
$80 - $90
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 36 Month(s)
Able to Provide Sponsorship

Skills

A/B Testing
Analytical Skill
Artificial Intelligence
Clustering
Data Analysis
Data Science
Decision-making
Evaluation
Extraction
Forecasting
Generative Artificial Intelligence (AI)
Good Clinical Practice
Google Cloud
Google Cloud Platform
Machine Learning (ML)
Management
Prompt Engineering
Regression Analysis
Statistical Models
Statistics
Training
Use Cases
Vertex

Job Details

We are looking for an experienced and forward-thinking Data Scientist to join our Data, Analytics, and Data Products team. The ideal candidate will have strong hands-on experience using Google Cloud Platform (Google Cloud Platform) tools especially Vertex AI and Gemini to deliver high-impact machine learning, statistical analysis, and generative AI solutions. This role plays a key part in optimizing decision-making and operational strategies across our core business functions.


Core Responsibilities

  • Data Analysis & Exploration
    • Analyze and explore large, complex datasets using BigQuery and other Google Cloud Platform-native sources.
    • Identify patterns, trends, and actionable insights to inform data science projects and business initiatives.
  • Statistical Modeling & Machine Learning
    • Build, tune, and evaluate ML models (e.g., classification, regression, clustering, forecasting).
    • Leverage Vertex AI (AutoML, custom training, BigQuery ML) for scalable model development.
  • Generative AI & LLM Applications
    • Develop and deploy generative AI use cases using Vertex AI and Gemini.
    • Engage in prompt engineering, model fine-tuning, feature extraction, summarization, and more.
    • Design and implement advanced architectures such as Retrieval-Augmented Generation (RAG).
  • Model Deployment & MLOps
    • Use Vertex AI Pipelines, Model Registry, and Prediction Services to deploy and manage models.
    • Implement monitoring for model drift, bias detection, and performance tracking in production.
  • Experimentation & Validation
    • Design and analyze experiments (e.g., A/B testing) to measure model effectiveness and business impact.
    • Define evaluation metrics aligned with model fairness, quality, and real-world utility.
  • Cross-Functional Collaboration
    • Work closely with Data Engineers, Product Managers, and business stakeholders to scope, design, and deploy models.
    • Translate complex analytical results into clear, strategic business insights.
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