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Austin, TX · Remote
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Austin, TX · Remote
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Austin, TX · Remote
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Houston, TX · On-site
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Houston, TX · On-site
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Weekend Data Science information
What is a weekend data science?
A Weekend Data Science job typically refers to a part-time or contract-based data science position where the primary work hours are on weekends. These roles are ideal for students, professionals seeking extra income, or those looking to gain experience in the data science field without committing to a full-time weekday schedule. Weekend data scientists analyze data, build models, and generate insights just like full-time data scientists but with flexible or reduced hours that fit around a weekend schedule.
What skills and qualifications are needed to thrive as a weekend data scientist?
To thrive as a Weekend Data Scientist, you need strong analytical skills, proficiency in statistics, and expertise in programming languages such as Python or R, often supported by a degree in a quantitative field. Familiarity with data analysis tools like SQL, machine learning libraries (e.g., scikit-learn, TensorFlow), and data visualization platforms (e.g., Tableau) is typically required. Excellent time management, problem-solving ability, and effective communication are crucial soft skills for delivering insights on tight weekend deadlines. These skills ensure that data-driven decisions can be made efficiently and accurately, even within limited time frames.
What challenges do data scientists working on weekends face, and how can they be managed?
Data scientists working weekend shifts often encounter challenges such as limited access to colleagues for collaboration or support, since many team members may not be available outside standard business hours. Additionally, urgent issues or data anomalies may require quick, independent problem-solving. Proactive communication with weekday teams, thorough documentation, and setting up clear protocols for handoffs can help manage these challenges and ensure smooth workflow continuity.
What is the difference between Weekend Data Science vs Part-Time Data Analyst?
| Aspect | Weekend Data Science | Part-Time Data Analyst |
|---|---|---|
| Credentials | Typically requires a degree in data science, statistics, or related field | Often requires a degree or relevant experience in data analysis or related fields |
| Work Environment | Project-based, flexible hours, often remote or on-site during weekends | Flexible hours, may be remote or on-site, often with less technical complexity |
| Industry Usage | Used in tech, finance, healthcare, and startups for specialized projects | Common in retail, marketing, and small businesses for routine data tasks |
Weekend Data Science roles focus on complex data projects requiring advanced skills, often during weekends, while Part-Time Data Analysts handle routine data tasks with less technical depth, offering flexible schedules. Both roles serve different needs but share a focus on data work outside standard hours.
Do weekend data scientists work on weekends?
Weekend data scientists may work on weekends depending on project deadlines, company policies, or client needs. Typically, data science roles involve regular weekday hours, but some positions require weekend work, especially in roles with flexible or project-based schedules. It is important to clarify work hours during the hiring process or in job descriptions.
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 are popular job titles related to Weekend Data Science jobs in Texas?
For Weekend Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Weekend Data Science jobs in Texas look for?
The top searched job categories for Weekend Data Science jobs in Texas are:
What cities in Texas are hiring for Weekend Data Science jobs?
Cities in Texas with the most Weekend Data Science job openings:
Job description
Job Summary:
TBK Bank, SSB is seeking a Director of Data Science to design, build, and operationalize quantitative models that power both internal and customer-facing intelligence solutions. This role focuses on developing forecasting, optimization, and signal-based models to translate large-scale transportation data into trusted insights.
Responsibilities:
• Develop and maintain forecasting and predictive models supporting transportation use cases such as pricing, demand forecasting, capacity trends, service performance, and network dynamics.
• Build and scale the data science function, including hiring, onboarding, and managing direct reports as business needs evolve. Design and execute statistical modeling and experimentation, including hypothesis testing, A/B testing, and causal analysis to evaluate market and operational changes.
• Build optimization and decision support models that inform routing, capacity allocation, pricing strategy, and operational trade‑offs.
• Lead signal development for transportation intelligence products, transforming raw transactional and network data into scalable, reliable indicators and indices.
• Establish and lead model validation, performance monitoring, and governance frameworks to ensure stability, accuracy, and trustworthiness of production models.
• Partner closely with product, analytics, and engineering teams to translate transportation domain needs into analytically sound, production ready models.
• Document methodologies, assumptions, and limitations to support transparency, internal review, and customer facing confidence in intelligence outputs.
• Continuously evaluate new data sources, modeling approaches, and techniques relevant to transportation, logistics, and network-based intelligence.
Qualifications:
Required:
• A Master's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or another relevant quantitative discipline is required.
• 5–7 years of professional experience in data science, applied statistics, or quantitative analytics.
• Strong experience with forecasting, predictive modeling, and statistical analysis in applied business contexts.
• Demonstrated ability to build models that support decision making, optimization, or market intelligence.
• Strong Python and SQL skills and experience working with large, complex datasets.
• Experience validating models and monitoring performance in production environments. Direct experience with model governance frameworks.
• Ability to clearly communicate quantitative insights to both technical and non‑technical stakeholders.
Preferred:
• Experience working with transportation, logistics, supply chain, or network-based data.
• Strong expertise in Regression & tree-based models (e.g., XGBoost, Random Forest)
• Strong expertise in Time series forecasting (e.g., SARIMAX, Prophet, TFT)
• Strong expertise in Statistical modeling of skewed distributions (e.g., log-normal, gamma)
• 2 years in a leadership or people‑management capacity
• Deep understanding of freight market dynamics, including the interaction between spot and contract pricing, broker and carrier economics, and the impact of capacity cycles and seasonality on market behavior.
• Familiarity with time‑series modeling, signal processing, or index construction.
• Experience supporting intelligence, analytics, or data products used by external customers.
• Experience working with large-scale datasets in cloud environments and data pipelines (e.g., Snowflake, AWS, Sagemaker)
Company:
A State Savings Bank headquartered in Dallas, Texas, TBK Bank is a subsidiary of Triumph Financial. (Nasdaq: TFIN). Founded in 2015, the company is headquartered in Dallas, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
TBK Bank, SSB is seeking a Director of Data Science to design, build, and operationalize quantitative models that power both internal and customer-facing intelligence solutions. This role focuses on developing forecasting, optimization, and signal-based models to translate large-scale transportation data into trusted insights.
Responsibilities:
• Develop and maintain forecasting and predictive models supporting transportation use cases such as pricing, demand forecasting, capacity trends, service performance, and network dynamics.
• Build and scale the data science function, including hiring, onboarding, and managing direct reports as business needs evolve. Design and execute statistical modeling and experimentation, including hypothesis testing, A/B testing, and causal analysis to evaluate market and operational changes.
• Build optimization and decision support models that inform routing, capacity allocation, pricing strategy, and operational trade‑offs.
• Lead signal development for transportation intelligence products, transforming raw transactional and network data into scalable, reliable indicators and indices.
• Establish and lead model validation, performance monitoring, and governance frameworks to ensure stability, accuracy, and trustworthiness of production models.
• Partner closely with product, analytics, and engineering teams to translate transportation domain needs into analytically sound, production ready models.
• Document methodologies, assumptions, and limitations to support transparency, internal review, and customer facing confidence in intelligence outputs.
• Continuously evaluate new data sources, modeling approaches, and techniques relevant to transportation, logistics, and network-based intelligence.
Qualifications:
Required:
• A Master's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or another relevant quantitative discipline is required.
• 5–7 years of professional experience in data science, applied statistics, or quantitative analytics.
• Strong experience with forecasting, predictive modeling, and statistical analysis in applied business contexts.
• Demonstrated ability to build models that support decision making, optimization, or market intelligence.
• Strong Python and SQL skills and experience working with large, complex datasets.
• Experience validating models and monitoring performance in production environments. Direct experience with model governance frameworks.
• Ability to clearly communicate quantitative insights to both technical and non‑technical stakeholders.
Preferred:
• Experience working with transportation, logistics, supply chain, or network-based data.
• Strong expertise in Regression & tree-based models (e.g., XGBoost, Random Forest)
• Strong expertise in Time series forecasting (e.g., SARIMAX, Prophet, TFT)
• Strong expertise in Statistical modeling of skewed distributions (e.g., log-normal, gamma)
• 2 years in a leadership or people‑management capacity
• Deep understanding of freight market dynamics, including the interaction between spot and contract pricing, broker and carrier economics, and the impact of capacity cycles and seasonality on market behavior.
• Familiarity with time‑series modeling, signal processing, or index construction.
• Experience supporting intelligence, analytics, or data products used by external customers.
• Experience working with large-scale datasets in cloud environments and data pipelines (e.g., Snowflake, AWS, Sagemaker)
Company:
A State Savings Bank headquartered in Dallas, Texas, TBK Bank is a subsidiary of Triumph Financial. (Nasdaq: TFIN). Founded in 2015, the company is headquartered in Dallas, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About TBK Bank
Sourced by ZipRecruiter
Company size
1,001 - 5,000 Employees
Headquarters location
Dallas, TX, US
Year founded
2015