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Data Science Assistant Jobs in Texas (NOW HIRING)

... data science solutions within SWBC's modern, cloud-native data ecosystem and enterprise AI/ML ... Contribute to the development and enhancement of Clara, SWBC's AI decision assistant, including ...

Responsibilities : • Act as the technical lead for high-impact data science projects, from ... decision assistant, including context engineering within Omni, model evaluation, and continuous ...

Independently work on data science projects and deliver innovative technical solutions to solve ... Assist engagement with key business stakeholders in discussion on business strategies and ...

... Data Science function by preparing data, conducting defined components of advanced quantitative ... Validate analytical outputs, test assumptions, review results for accuracy, and assist with ...

... Data Science function by preparing data, conducting defined components of advanced quantitative ... Validate analytical outputs, test assumptions, review results for accuracy, and assist with ...

... science activities. * Develop and maintain robust data pipelines that support the ingestion ... * Assist in preparing technical data tables, figures, and interpretations for reports and ...

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Data Science Assistant information

What are Data Science Assistants?

Data Science Assistants are professionals who support data scientists and analytics teams by handling tasks such as data collection, data cleaning, preparing datasets, conducting preliminary analyses, and creating visualizations. They often work with large datasets, assist in maintaining data integrity, and help automate routine processes. Their role allows data scientists to focus on more complex modeling and analytical work, making the overall workflow more efficient. Data Science Assistants typically have a foundational understanding of statistics, programming (such as Python or R), and data management tools.

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

To thrive as a Data Science Assistant, you need a solid understanding of statistics, data analysis, and programming (often with a background in mathematics, computer science, or a related field). Familiarity with tools like Python or R, data visualization software, and experience with databases or spreadsheet systems are typically required. Attention to detail, strong problem-solving abilities, and effective communication set outstanding candidates apart. These skills are crucial for supporting data-driven decision-making and ensuring accurate, actionable insights for organizations.

Is 40 too late for data science?

Data Science Assistants and other data science roles do not have strict age limits; many professionals start or transition into data science later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned at any age through online courses, certifications, and practical experience.

How does a Data Science Assistant typically collaborate with data scientists and other team members on projects?

As a Data Science Assistant, you will frequently support data scientists by preparing datasets, conducting preliminary data analysis, and creating visualizations. You will often work closely with analysts, engineers, and subject matter experts to gather requirements and ensure data is cleaned and formatted appropriately. Collaboration is a key part of the role, as you may participate in team meetings, share findings, and help with documentation to keep projects running smoothly. This supportive environment provides an excellent opportunity to learn from experienced professionals and gain exposure to the full data science workflow.

What is the 80 20 rule in data science?

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

What is the difference between Data Science Assistant vs Data Analyst?

AspectData Science AssistantData Analyst
Required CredentialsBachelor's in Data Science, Statistics, or related fieldBachelor's in Statistics, Mathematics, or related field
Work EnvironmentTech companies, research labs, data-driven departmentsBusiness, finance, marketing, healthcare sectors
Employer & Industry UsageUsed in data science teams for supporting models and analysisUsed across industries for interpreting data and generating reports

While both roles involve working with data, a Data Science Assistant typically supports data science projects, focusing on data preparation and model testing. A Data Analyst primarily interprets data to generate insights and reports. The roles overlap in skills and work environments but differ in their core responsibilities and focus areas.

What do data assistants do?

Data Science Assistants support data analysis by collecting, cleaning, and organizing data sets. They often use tools like Excel, SQL, or Python to prepare data for modeling and reporting, assisting data scientists and analysts in project workflows.

Can I get a data scientist job with no experience?

Entry-level data science assistant roles often do not require prior experience, but candidates typically need a strong foundation in programming (such as Python or R), statistics, and data analysis. Gaining relevant skills through online courses, certifications, or personal projects can improve chances of securing such positions.
What are the most commonly searched types of Data Science jobs in Texas? The most popular types of Data Science jobs in Texas are:
What cities in Texas are hiring for Data Science Assistant jobs? Cities in Texas with the most Data Science Assistant job openings:
Infographic showing various Data Science Assistant job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.
Data Scientist II

Data Scientist II

Swbc

San Antonio, TX • On-site

Full-time

Medical, Retirement

Posted 28 days ago


SWBC rating

6.5

Company rating: 6.5 out of 10

Based on 17 frontline employees who took The Breakroom Quiz


Job description

SWBC is seeking a talented individual who will contribute to the development of machine learning models, analytical solutions, and AI-driven capabilities that directly impact business performance and client outcomes. This role works closely with the Senior Data Scientist, Analytics Engineers, and Data Analysts to design, build, and deploy data science solutions within SWBC's modern, cloud-native data ecosystem and enterprise AI/ML platform. The Data Scientist II is expected to independently lead analyses and model development projects with minimal guidance, while contributing to the broader AI strategy of the organization.

Why you'll love this role:

In this role, you will work hands-on with cutting-edge tools and platforms every day, including Hex for experimentation, Omni for AI context development powering Clara, and SWBC Intelligence our multi-model AI orchestration platform spanning AWS Bedrock and AWS Sagemaker to build and deploy intelligent solutions for internal and client-facing use cases. We offer a collaborative environment that values continuous learning, empirical rigor, and professional growth.

Essential duties include the following:

  • Design, develop, and deploy machine learning models and analytical solutions to address business problems, including forecasting, segmentation, classification, and anomaly detection.
  • Contribute to the development and enhancement of Clara, SWBC's AI decision assistant, including supporting context engineering within Omni, prompt development, and model evaluation workflows.
  • Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning.
  • Develop and deploy AI/ML solutions within SWBC Intelligence, the enterprise multi-model AI orchestration platform, leveraging AWS Bedrock and AWS Sagemaker as directed by senior team members.
  • Conduct exploratory data analysis and feature engineering using data sourced from SWBC's governed medallion architecture (Bronze - Silver - Gold) in Snowflake.
  • Participate in the platform's Evaluation Harness process, including test case development, model benchmarking, and scoring framework contribution to ensure solution quality.
  • Perform AI experimentation and prototyping within governed sandbox environments, including Snowflake Sandbox and Hex, ensuring adherence to data privacy and compliance requirements.
  • Apply Responsible AI principles, including bias detection, model explainability, and fairness monitoring, to all model development activities.
  • Collaborate with cross-functional teams including Analytics Engineering, Data Management, and business stakeholders to translate business problems into data science solutions.
  • Communicate findings, model results, and recommendations to both technical and non-technical audiences through clear documentation, visualizations, and presentations.
  • Stay current with emerging trends in data science, machine learning, and AI, and contribute to the team's knowledge sharing and continuous improvement culture.

Serious candidates will possess the minimum qualifications:

  • Master's degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or a related discipline. Equivalent professional experience (5+ years) may be considered in lieu of an advanced degree.
  • Minimum of two (2) years of progressive experience in data science, analytics, or machine learning roles.
  • Proficiency in Python and SQL, with working knowledge of ML frameworks such as TensorFlow, PyTorch, scikit-learn, or equivalent.
  • Experience developing and deploying models within cloud-based AI/ML platforms, preferably AWS (SageMaker, S3) and Snowflake.
  • Familiarity with MLOps tools and practices, including MLflow, experiment tracking, and model versioning.
  • Proficiency in Hex for data science experimentation and prototyping.
  • Understanding of statistical modeling, hypothesis testing, and experimental design.
  • Ability to work with structured and semi-structured data, perform data cleaning, and conduct exploratory analysis.
  • Strong communication skills with the ability to present findings to both technical and non-technical audiences.
  • Ability to interpret ambiguity and work with minimal direction on defined project scopes.
  • Ability to lift 20 lbs. of files, supplies, documents, or other related items.

SWBC offers*:

  • Competitive overall compensation package
  • Work/Life balance
  • Employee engagement activities and recognition awards
  • Years of Service awards
  • Career enhancement and growth opportunities
  • Leadership Academy and Mentor Program
  • Continuing education and career certifications
  • Variety of healthcare coverage options
  • Traditional and Roth 401(k) retirement plans
  • Lucrative Wellness Program

*Based upon employee eligibility

Additional Information:

SWBC is a Substance-Free Workplace and requires pre-employment drug testing.

Please note, SWBC does not hire tobacco users as allowed by law.

To learn more about SWBC, visit our website at www.SWBC.com. If interested, please click the appropriate apply button.


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