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Hourly Google Data Science Jobs (NOW HIRING)

Altagrove LLC

Data Science

Norfolk, VA · On-site

Experience with AWS, Azure, Google Cloud, or hybrid cloud environments. * Understanding of data ... Bachelors or Masters degree in Data Science, Computer Science, Data Engineering, Mathematics ...

Facet

Lead Data Scientist

$180K - $220K/yr

Requirements * 7+ years of experience in a data science or machine learning role * Experience with at least one dashboarding tool, such as: Tableau, Power BI, Looker, Google Data Studio, Streamlit ...

Luster National

Reporting Data Scientist

Sacramento, CA · On-site

Master's degree in data science, computer science, statistics, engineering, business analytics, or ... Project Management Professional (PMP), Microsoft Power BI Data Analyst, Google Data Analytics, MBA, ...

Opus Inspection

Data Analyst Intern

Tucson, AZ · On-site

Currently pursuing or recently completed a degree in Data Science, Statistics, Information ... Familiarity with data visualization tools like Tableau, Power BI, or Google Data Studio.

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How much do hourly google data science jobs pay per year?

As of Jul 11, 2026, the average yearly pay for hourly google data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is the hourly rate for a data scientist?

The hourly rate for a data scientist varies based on experience, location, and industry, but typically ranges from $30 to $100 per hour. Entry-level data scientists may earn closer to the lower end, while experienced professionals with specialized skills in machine learning and data analysis tools can command higher rates.

What are the typical daily responsibilities of an Hourly Google Data Science role?

As an Hourly Google Data Science professional, you can expect to work on data cleaning, exploratory data analysis, and supporting ongoing projects with statistical insights. Your day may involve collaborating with product managers and engineers to define metrics, analyze user behavior, and present findings through clear visualizations. Because the role is hourly, tasks are often project-based with a strong emphasis on timely deliverables and adapting to shifting priorities. Collaboration tools and regular check-ins help ensure alignment with the team and project goals.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist; many professionals transition into data science later in their careers. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications, regardless of age.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or data. Data scientists often focus on the most impactful features or data subsets to optimize model performance and efficiency.

What are the key skills and qualifications needed to thrive as an Hourly Google Data Scientist, and why are they important?

To thrive as an Hourly Google Data Scientist, you generally need strong analytical skills, a background in statistics or computer science, and experience with data modeling and analysis. Familiarity with programming languages like Python or R, as well as tools such as SQL, TensorFlow, and Google Cloud Platform, is typically required. Exceptional problem-solving abilities, communication skills, and a collaborative mindset help individuals stand out in this role. These skills are crucial for extracting actionable insights from data, effectively presenting findings, and driving impactful decisions within Google's fast-paced environment.

How much does Google pay a data scientist?

Google data scientists typically earn an average salary ranging from $120,000 to $170,000 per year, depending on experience, location, and level. Compensation may also include bonuses, stock options, and benefits, with roles often requiring proficiency in machine learning, programming, and data analysis tools.

What is an Hourly Google Data Science job?

An Hourly Google Data Science job refers to a data science role at Google where an individual is compensated based on the number of hours worked, rather than receiving a fixed annual salary. These positions may be contract-based, freelance, or part of a temporary staffing arrangement. Hourly data scientists at Google typically analyze large datasets, build predictive models, and help inform business decisions using statistical and machine learning techniques. This role requires strong analytical skills, programming expertise, and familiarity with tools such as Python, R, and SQL. It can be a good option for those seeking flexible work arrangements or project-based employment.
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Hourly Google Data Science jobs near you
Data Science

Data Science

Altagrove LLC

Norfolk, VA • On-site

Full-time

Posted 9 days ago

Job description

Salary:

Who we are:


Altagrove delivers smart and innovative technology solutions that create competitive advantages for our customers and their missions. Our focus areas include Space, Connectivity, Cyber, Cloud, Analytics, and Research & Development. As we continue to grow, Altagrove is actively recruiting for a Data Scientist to join our energetic and entrepreneurial team that is executing on a variety of projects that are technology oriented. A successful candidate will bring a core area of expertise and a passion for learning and implementing new ideas in a start-up environment.


Follow us at -https://www.linkedin.com/company/altagrove


What you will do:


  • Design, develop, deploy, and maintain AI-enabled analytics solutions supporting operational and strategic mission objectives.
  • Build and optimize enterprise data pipelines, ingestion frameworks, transformation workflows, and integration services supporting analytics and AI platforms.
  • Develop machine learning models, predictive analytics capabilities, and decision-support solutions using structured and unstructured data.
  • Design and implement Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) architectures, vector databases, and AI-enabled knowledge management capabilities.
  • Develop scalable data architectures, metadata enrichment pipelines, indexing services, and retrieval systems supporting enterprise knowledge exploitation.
  • Collaborate with mission stakeholders, engineers, and technical teams to identify high-value AI and data-driven use cases.
  • Conduct data exploration, feature engineering, model training, validation, testing, and performance optimization activities.
  • Design and implement ETL/ELT processes supporting operational, analytical, and machine learning workloads.
  • Develop dashboards, visualizations, reports, and analytical products that communicate insights to technical and non-technical stakeholders.
  • Support cloud-native and hybrid data environments leveraging AWS, Azure, Kubernetes, and modern data engineering technologies.
  • Implement data quality controls, monitoring solutions, security controls, and governance practices across enterprise data environments.
  • Work closely with Cloud Engineers, DevSecOps Engineers, and Software Developers to support end-to-end solution delivery.
  • Research emerging AI, machine learning, and data engineering technologies and recommend innovative applications for customer missions.
  • Support technical documentation, architecture development, operational procedures, training materials, and knowledge transfer activities.


What you will bring:


  • Experience developing and deploying advanced analytics, machine learning, artificial intelligence, or enterprise data engineering solutions within government, defense, intelligence, or highly regulated environments.
  • Strong proficiency in Python and experience with modern data science and engineering frameworks.
  • Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or similar technologies.
  • Experience designing and maintaining enterprise data pipelines, ETL/ELT workflows, and data integration architectures.
  • Familiarity with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering concepts.
  • Experience working with SQL, NoSQL, data lakes, data warehouses, and cloud-native data platforms.
  • Experience with AWS, Azure, Google Cloud, or hybrid cloud environments.
  • Understanding of data governance, metadata management, data quality, security, and compliance principles.
  • Experience with containerization technologies, Kubernetes, DevSecOps practices, and Infrastructure-as-Code is highly desirable.
  • Strong analytical, problem-solving, and communication skills.
  • Ability to collaborate effectively across multidisciplinary engineering and mission teams.
  • Experience supporting NATO, DoD, Intelligence Community, or other national security organizations is highly desired.
  • Bachelors or Masters degree in Data Science, Computer Science, Data Engineering, Mathematics, Statistics, Engineering, Information Systems, or a related discipline.
  • Active Secret Clearance required; TS/SCI or NATO Secret preferred.
  • Exceptional attention to detail and organizational skills.