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Google Data Science Jobs in Tennessee (NOW HIRING)

... Google Cloud Platform (GCP) ecosystems. * Lead and own high-impact data science initiatives within the DW 2.0 data monetization scope and other initiatives as needed, from problem framing through ...

AI and Data Science Engineer III

Nashville, TN · On-site +1

$110K - $132K/yr

... or Google Cloud Platform for data platforms and scalable compute * 4+ years of experience ... AI and Data Science Engineer III Position Summary Our Deloitte Human Capital team transforms ...

Databricks Data Engineer

Nashville, TN · On-site

$110K - $132K/yr

Required : • Bachelor's degree in Computer Science, Engineering, Information Systems, Data ... Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) • Ability to travel 50 ...

... Science, Artificial Intelligence and Robotics - At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud ...

D. in Computer Science, Data Science, or related field. • Proven experience as an AI/ML Architect ... Preferred Skills • Experience with cloud platforms such as AWS, Azure, or Google Cloud. • ...

Databricks Data Engineer

Nashville, TN · On-site

$110K - $132K/yr

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Mathematics ... Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) * Ability to travel 50 ...

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Google Data Science information

See Tennessee salary details

$24.7K

$117K

$202.4K

How much do google data science jobs pay per year?

As of Jun 16, 2026, the average yearly pay for google data science in Tennessee is $117,017.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,692.00 and $146,860.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Data scientists, including those working at Google, can earn $300,000 or more annually, especially with senior roles, extensive experience, advanced skills in machine learning and big data tools, and in high-cost-of-living areas. Compensation often includes base salary, bonuses, and stock options, which contribute to total earnings at this level.

What are the key skills and qualifications needed to thrive in the Google Data Science position, and why are they important?

To thrive as a Google Data Science professional, you need a strong foundation in statistical analysis, machine learning, and data manipulation, often supported by a degree in a quantitative field such as computer science, statistics, or mathematics. Proficiency in programming languages like Python or R, experience with large-scale data processing tools (such as SQL, TensorFlow, or BigQuery), and familiarity with cloud-based platforms are commonly required. Excellent problem-solving, communication, and collaboration skills help set candidates apart in effectively translating complex data insights to varied stakeholders. These capabilities are crucial for driving impactful, data-driven decisions within cross-functional teams at Google.

How much does Google pay a Data Scientist?

Google Data Scientists typically earn a base salary ranging from $120,000 to $180,000 annually, with total compensation often including bonuses and stock options that can increase overall earnings. Compensation varies based on experience, location, and skill level, with advanced skills in machine learning and data analysis tools being highly valued.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist, including at age 40. Success in data science depends on skills, experience, and continuous learning of tools like Python, R, and SQL, rather than age. Many professionals transition into data science later in their careers and find opportunities with relevant certifications and a strong portfolio.

What is a Google Data Science job?

A Google Data Science job involves analyzing large datasets to provide insights and drive data-informed decisions. Data scientists at Google apply statistical modeling, machine learning, and analytical techniques to solve complex problems in products like Search, Ads, YouTube, and Cloud. They work closely with engineers, product managers, and business teams to develop data-driven solutions. Strong coding skills in Python or SQL, experience with big data tools, and a solid foundation in statistics are essential for this role.

What types of projects do Google Data Science professionals typically work on?

Google Data Science professionals engage in a wide variety of impactful projects, such as optimizing algorithms for product recommendations, improving user experiences through data-driven insights, and developing predictive models to inform business strategies. They often work closely with product managers, engineers, and designers to translate complex data findings into actionable solutions. The work environment is highly collaborative and fast-paced, with opportunities to contribute to innovative initiatives across different Google products and services. This dynamic setting allows data scientists to continuously expand their skill sets and take on new challenges, fostering both personal and professional growth.

What is the average salary for a Data Scientist at Google?

The average salary for a Data Scientist at Google is approximately $120,000 to $150,000 per year, depending on experience, location, and level. Senior roles or those with specialized skills in machine learning and data analysis can earn higher compensation, often including bonuses and stock options.
What are the most commonly searched types of Google Data Science jobs in Tennessee? The most popular types of Google Data Science jobs in Tennessee are:
What are popular job titles related to Google Data Science jobs in Tennessee? For Google Data Science jobs in Tennessee, the most frequently searched job titles are:
What cities in Tennessee are hiring for Google Data Science jobs? Cities in Tennessee with the most Google Data Science job openings:
Infographic showing various Google Data Science job openings in Tennessee as of June 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $117,017 per year, or $56.3 per hour.

Full-time

Medical, Dental, Vision, Retirement

Posted 4 days ago


Job description

Provide dynamic leadership at the enterprise level related to vast quantities of data (including disparate data), enterprise analytics, statistical modeling, reporting projects and initiatives.

Essential Functions

About the Role - The Data Scientist Principal leads advanced analytics initiatives across the organization, designs end-to-end machine learning solutions, and mentors data science teams. This individual partners closely with stakeholders in product, engineering, and business units to translate complex data into actionable insights that drive strategic decision-making and innovation. Takes end-to-end ownership and responsibility over critical data science initiatives. Advances DW 2.0's broad capabilities to use and deploy cutting edge data science and machine learning tools and methods in projects, platforms and products. Anchors current best practices by championing the design and build of reusable data science assets. Combines knowledge of data scientific methods, CI/CD, statistics, and machine learning / data engineering practices to provide recommendations on the most organizationally critical and complex problems. Advises junior data scientists, managers, and those in less senior positions. You will contribute to our mission within a collaborative, mentorship-driven environment, working hands-on with the Azure and / or Google Cloud Platform (GCP) ecosystems. 

  • Lead and own high-impact data science initiatives within the DW 2.0 data monetization scope and other initiatives as needed, from problem framing through deployment and monitoring. 
  • Apply advanced AI techniques (LLMs, reinforcement learning, graph ML, and optimization algorithms) to address complex, high-impact challenges across supply chain operations such as routing, inventory balancing, capacity planning, and risk mitigation. 
  • Architect and develop scalable machine learning models (e.g., predictive, prescriptive, NLP, computer vision) for production. 
  • Lead rapid prototyping and iterative experimentation: leverage modern AI stacks (Azure OpenAI, Vertex AI, LangChain, vector databases) to design lean experiments, measure impact, and iterate quickly while avoiding dependency bottlenecks. 
  • Handle pressure with poise, balancing urgent requests with long-term project goals and ensuring reliable outcomes. 
  • Build and operationalize end-to-end AI/ML systems, including LLM pipelines, prompt engineering strategies, fine-tuning, model evaluation, guardrails, and monitoring for performance, drift, and responsible AI compliance. Collaborate with engineering teams to integrate data pipelines, ensure model reliability, and optimize performance. 
  • Define metrics and success criteria; perform rigorous statistical analysis, A/B testing, and AI model evaluation (hallucination detection, accuracy, latency, relevance) to validate system effectiveness. 
  • Translate business objectives into analytical approaches, interpret results, and present clear storytelling and data-driven recommendations to senior leadership. 
  • Mentor and coach junior and mid-level data scientists; establish best practices in coding, model development, and documentation. 
  • Drive innovation by researching and prototyping emerging techniques, tools, and frameworks in machine learning, deep learning, and AI. 
  • Partner with legal, security, and data governance teams to ensure data usage adheres to privacy, security, and contractual obligations; develop compliant mechanisms to enable safe, fast experimentation (see recommended approaches below). 
  • Communicate results and recommendations clearly to senior leadership and cross-functional stakeholders; translate complex analyses into actionable business insights. 
  • Cultivate deep domain expertise in FedEx data and tools, taking direction from senior team members and contributing to knowledge sharing. 
  • Collaborate with business partners and subject matter experts to translate complex questions into clear analytical insights and present findings effectively. 

Knowledge, Skills, and Abilities (required)

  • Extensive knowledge in advanced data science and machine learning methods, including the iterative development of analysis pipelines to provide insights at scale. 
  • Strong experience as a leader of multi-functional project teams. 
  • Excellent interpersonal skills and the ability to present and communicate effectively to executive audiences. A related advanced degree may offset the related experience requirements. 
  • Proficiency in SQL, Python, and/or R. 
  • Foundational knowledge of machine learning libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch). 
  • Hands-on experience with Azure (e.g., Data Factory, Synapse, Databricks, Blob Storage) and / or Google Cloud Platform (GCP) and its core analytics/ML services (BigQuery, Vertex AI, Dataflow, Pub/Sub). 
  • Demonstrated ability to leverage various APIs for data manipulation and integration. 
  • Experience with at least one data visualization tool or package (e.g., Tableau, Power BI, Spotfire, Shiny, Plotly, Matplotlib, Seaborn). 
  • Solid understanding of ETL concepts, relational databases (e.g., Teradata, Oracle), and working with large-scale datasets. 
  • Proficiency with version control (git) and familiarity with MLOps/DevOps principles (CI/CD, model tracking, deployment workflows).
  • Demonstrated superior analytical skills with diverse analytics, data types, and statistical software and applications. 
  • Outstanding Interpersonal skills, written, and oral communication skills. 
  • Proven Leadership skills. 

Preferred Skills and Experience 

  • Proven background with enterprise AI solutions, agentic architectures, or scalable LLM platforms. 
  • Proficiency in Power BI for building interactive dashboards, reports, and data visualizations. 
  • Experience in your industry domain (e.g., finance, healthcare, e-commerce). 
  • Familiarity with MLOps practices and tools (MLflow, Kubeflow, Airflow). 
  • Prior experience leading cross-functional teams and managing stakeholder relationships. 
  • Publications or contributions to open-source projects in machine learning or data science.
  •  

Minimum Education

Master's degree or equivalent in a quantitative discipline required.

Minimum Experience

Proven expert and nine (9) years work experience in innovative measurement and analysis, quantitative business problem solving, solutions implementation, operations analysis, marketing analysis, simulation development and/or predictive analytics.


DomicileInformation

This position is eligible for remote work and may be located anywhere within the United States excluding AK, HI and U.S. territories, however if you live within the 50 miles radius of a campus you will be required to work at a FedEx campus location several times per week. 


Preferred Qualifications:

Pay Transparency: This compensation range is provided as a reasonable estimate of the current starting salary range for this role across all potential locations. If this opportunity includes multiple job levels, the range is a reasonable estimate of the current starting salary for the lowest level to the current starting salary of the highest level. Actual starting pay would be determined by experience relative to the job, market level, pay at the location for this job and other job-related factors permitted by law. An employee may be eligible for additional pay, premiums, or bonus potential. The Company offers eligible employees health, vision and dental insurance, retirement, and tuition reimbursement.

Pay: US: $9,208.38/mo - $20,872.33/mo, CO: $9,208.38/mo - $20,002.65/mo, MD: $9,719.96/mo - $20,872.33/mo, NJ: $9,719.96/mo - $20,872.33/mo, NY: $9,719.96/mo - $20,872.33/mo, NYC: $11,766.26/mo - $20,872.33/mo, WA: $9,719.96/mo - $20,872.33/mo

Additional Details:


FedEx Dataworks is an Equal Opportunity Employer including, Vets/Disability.

  • Know Your Rights
  • Pay Transparency

Dataworks does not discriminate against qualified individuals with disabilities in regard to job application procedures, hiring, and other terms and conditions of employment. Further, Dataworks is prepared to make reasonable accommodations for the known physical or mental limitations of an otherwise qualified applicant or employee to enable the applicant or employee to be considered for the desired position, to perform the essential functions of the position in question, or to enjoy equal benefits and privileges of employment as are enjoyed by other similarly situated employees without disabilities, unless the accommodation will impose an undue hardship. If a reasonable accommodation is needed, please contact DataworksTalentAcquisition@corp.ds.fedex.com.

Employment Type: FULL_TIME