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Data Scientist Jobs in Springfield, TN (NOW HIRING)

Tao Digital Solutions, Inc.

Data Scientist

Nashville, TN · On-site

$60K/yr

Data Scientist Location : Hermitage, TN Wage : $60,000 Job Duties : * Data Acquisition, Cleaning & Preprocessing * * Assist in collecting, validating, and preprocessing structured and unstructured ...

Cisco

Applied AI Scientist

Nashville, TN · On-site

Collaborate with engineering, product, and data science teams to understand requirements, incorporate stakeholder feedback, and deliver AI/ML solutions that address business and technical needs.

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All JobsData Scientist JobsData Scientist Jobs in Springfield, TN

Data Scientist information

See Springfield, TN salary details

$35.4K

$115.9K

$185.6K

How much do data scientist jobs pay per year?

As of Jul 25, 2026, the average yearly pay for data scientist in Springfield, TN is $115,913.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,000.00 and $128,400.00 per year, depending on experience, location, and employer.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks, data visualization tools, and big data platforms like TensorFlow, Tableau, and Hadoop, as well as certifications in data science, are highly valued. Excellent problem-solving skills, curiosity, and the ability to communicate complex findings clearly set outstanding data scientists apart. These skills and qualities are crucial for extracting actionable insights from data, driving business decisions, and collaborating effectively with stakeholders.

What Do Data Scientists Do?

Data scientists collect, confirm, and interpret data to determine useful information for their employer. They help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. The information data scientists get from the records they gather helps businesses make major decisions in critical areas, such as product development, sales and marketing techniques, and client retention. Data scientists are highly educated; the majority of them have at least a master's degrees, and many have doctorates. Data scientists are valuable members of organizations in many different industries, including pharmaceuticals, manufacturing, and banking.

What careers can I do with data science?

Data scientists can pursue careers in fields such as machine learning engineering, data analysis, business intelligence, data engineering, and research roles. These positions often require skills in programming, statistical analysis, and tools like Python, R, or SQL, and may involve working in industries like finance, healthcare, technology, or marketing.

Is a data scientist job still in-demand?

Yes, data scientist roles remain in high demand across various industries due to the increasing reliance on data-driven decision making. Skills in machine learning, statistical analysis, and programming languages like Python or R are highly valued, and the field continues to grow as organizations seek to leverage big data for competitive advantage.

What are Data Scientists?

Data Scientists are professionals who use statistical, analytical, and programming skills to collect, analyze, and interpret large volumes of data. They extract insights and trends from complex data sets to help organizations make data-driven decisions. Data Scientists often work with machine learning, data mining, and big data technologies to build predictive models and solve business problems. Their work bridges the gap between technical data analysis and actionable business strategy.

What does a data scientist do exactly?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use statistical techniques, programming languages like Python or R, and tools such as SQL and machine learning algorithms to interpret data and solve complex problems.

Is 30 too late for data science?

Data scientists can enter the field at any age, including 30 or older, as success depends on skills, experience, and continuous learning. Many professionals transition into data science from different backgrounds by acquiring relevant skills such as programming, statistics, and machine learning through courses or certifications. Age is not a barrier if you develop a strong portfolio and stay current with industry tools and techniques.

What is the difference between Data Scientist vs Data Analyst?

AspectData Scientist
Required CredentialsDegree in Computer Science, Statistics, or related field; often requires advanced degrees
Work EnvironmentResearch and development, predictive modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcare, consulting firms
Common Search & ComparisonOften compared due to overlapping skills in data analysis and modeling

Data Scientists focus on building predictive models, advanced analytics, and machine learning, often requiring higher-level technical skills and education. Data Analysts primarily interpret existing data, generate reports, and support decision-making with descriptive analytics. While both roles analyze data, Data Scientists handle complex modeling and predictive tasks, whereas Data Analysts focus on data interpretation and reporting.

What are some typical projects Data Scientists work on, and how do they collaborate with other teams?

Data Scientists often work on projects such as building predictive models, analyzing large datasets to uncover trends, and developing data-driven solutions to business problems. They regularly collaborate with cross-functional teams, including software engineers, data engineers, and business analysts, to ensure that their insights are actionable and aligned with business goals. Effective communication and teamwork are essential, as Data Scientists frequently need to present complex findings to non-technical stakeholders and incorporate feedback from various departments.
More about Data Scientist jobs
What are the most commonly searched types of Data Scientist jobs in Springfield, TN? The most popular types of Data Scientist jobs in Springfield, TN are:
What are popular job titles related to Data Scientist jobs in Springfield, TN? For Data Scientist jobs in Springfield, TN, the most frequently searched job titles are:
What cities near Springfield, TN are hiring for Data Scientist jobs? Cities near Springfield, TN with the most Data Scientist job openings:
Data Scientist jobs near you
Infographic showing various Data Scientist job openings in Springfield, TN as of July 2026, with employment types broken down into 1% As Needed, 80% Full Time, 13% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $115,913 per year, or $55.7 per hour.

Data Scientist

Tao Digital Solutions, Inc.

Nashville, TN • On-site

$60K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Title : Data Scientist
Location : Hermitage, TN
Wage : $60,000
Job Duties :
  1. Data Acquisition, Cleaning & Preprocessing

    • Assist in collecting, validating, and preprocessing structured and unstructured datasets from internal and third-party financial systems.
    • Perform data quality checks, resolve anomalies, and maintain metadata using SQL, Python (Pandas), and Excel.

  1. Exploratory Data Analysis (EDA)

    • Conduct exploratory data analysis to identify trends, outliers, and correlations within financial and operational datasets.
    • Support the preparation of data summaries, distribution checks, and hypothesis validations.

  1. Automation & Data Pipeline Support

    • Assist in developing automation scripts and data pipelines using Python, Excel macros, and RPA tools (e.g., Blue Prism) to streamline data ingestion and transformation.
    • Support version control and CI/CD practices using Git repositories.

  1. Predictive Modeling & Forecasting

    • Support senior data scientists in building and validating statistical and machine learning models to forecast revenue trends, customer churn, or financial health.
    • Participate in refining time-series models and basic regressions using Python (Scikit-learn, StatsModels).

  1. Financial & Business Analysis

    • Contribute to financial modeling by evaluating key metrics (e.g., EBITDA, revenue growth, margins) and integrating external macroeconomic indicators into models.
    • Work alongside business analysts to align technical models with stakeholder requirements.

  1. Data Visualization & Dashboarding

    • Develop and maintain interactive dashboards using Tableau, Power BI, or Python (Matplotlib, Seaborn) to communicate insights to internal stakeholders.
    • Automate reporting templates and visualization tools for monthly and quarterly updates.

  1. Documentation & Compliance

    • Maintain comprehensive documentation for model assumptions, workflows, data dictionaries, and QA protocols.
    • Ensure data practices align with internal governance policies and industry regulations (e.g., GDPR, SOX).

  1. Collaboration & Communication

    • Work closely with cross-functional teams including finance, data engineering, and business strategy teams to align analytical efforts with organizational goals.
    • Participate in sprint meetings and contribute to shared knowledge repositories.

  1. Model Monitoring & Feedback Loops

    • Assist in tracking model performance and accuracy post-deployment using standard KPIs (e.g., RMSE, MAE).
    • Help integrate user feedback and error analysis into model retraining cycles.

  1. Professional Development

    • Attend internal workshops and training sessions on data science tools and methodologies.
    • Stay informed of advancements in machine learning, financial modeling, and analytics platforms.

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