Data Science Manager Jobs in Texas
Data Science Manager jobs in Texas are in strong demand, concentrated in technology, financial services, energy, and healthcare sectors, with openings at every level from team lead to senior director. Austin, Dallas, and Houston anchor most of the hiring activity, where companies like Dell Technologies, JPMorgan Chase, and ExxonMobil consistently staff data science leadership roles. The most sought-after specialties in Texas are machine learning infrastructure, business intelligence strategy, and applied analytics in energy and fintech. Find a role that fits below and apply directly.
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The position of Senior Consultant - Data Science & AI is within the TTEC Digital Analytics team. The Analytics group is responsible for Data Science, Advanced Analytics, Generative AI, and Engineering projects that include the design and validation of predictive models, building scalable machine learning pipelines, engineering autonomous agentic workflows, and leveraging enterprise Gen AI (LLMs, RAG architectures) to convert raw, unstructured client data into actionable insights that drive strategic business decision-making.
Key responsibilities
- Develop Machine Learning Models: End-to-end development, training, and deployment of production-grade predictive and prescriptive models (e.g., propensity modeling, customer segmentation, demand forecasting).
- Design & Deploy Generative AI Solutions: Architect, fine-tune, and evaluate enterprise Gen AI systems—including Retrieval-Augmented Generation (RAG) pipelines, custom LLM applications, and prompt engineering frameworks—to extract insights from unstructured data.
- Architect Agentic AI Systems: Design and deploy autonomous AI agents and multi-agent workflows capable of multi-step reasoning, tool utilization, and task automation to solve complex client business challenges.
- Deliver Advanced Analytics: Help clients maximize the efficiency of their strategic initiatives by building advanced analytics models that measure campaign performance, predict customer behavior, and maximize long-term ROI.
- Data Storytelling & Visualization: Synthesize complex algorithmic outcomes and machine learning metrics into compelling, executive-ready presentations and dashboards that clearly articulate business value and ROI.
- Develop and Maintain Analytics Workflows: Analyze raw client data and build optimized analytics datasets within cloud environments to support modeling and reporting.
- Lead workshops with external clients to uncover business objectives, map out data landscapes, and translate vague business problems into structured data science methodologies.
- Create solutions and design documentation for client data science architectures and ML pipelines.
- Work on projects independently as well as being part of a large team across multiple client engagements.
- Crosstrain Junior Data Scientists/Analysts or other team members with your area of expertise, providing technical leadership and code reviews.
- Further develop skills both on the job and through formal learning channels to stay ahead of AI/ML trends.
- Assist in pre-sales activities by scoping new client opportunities and providing accurate work/effort estimates.
Skills and Experience Requirements
- Post-Secondary Degree (or Diploma) related to Data Science, Statistics, Computer Science, Economics, Business Analytics, or an IT-related field.
- 5–8 years of total experience in Data Science, Advanced Analytics, or Management Consulting.
- 3+ years of experience in an external client-facing consulting or professional services capacity.
- 3+ years of application, model design, and deployment experience natively within a cloud environment (GCP preferred).
- 1–2+ years of hands-on experience architecting and deploying Generative AI systems (e.g., RAG pipelines, fine-tuned LLMs) and Agentic AI workflows (e.g., autonomous agents, tool-use integration).
- Demonstrated experience delivering both traditional predictive models and modern AI solutions to enterprise stakeholders.
- Google Cloud Certified Professional Data Engineer, Professional Machine Learning Engineer, or equivalent Generative AI/Cloud Certifications (Highly Preferred).
- Python Stack: pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow, Transformers
- Generative & Agentic AI Frameworks: LangChain, LlamaIndex, AutoGen/CrewAI, Prompt Engineering, RAG architectures, Multi-Agent Orchestration, Function Calling & Tool Integration
- Vector Databases & Search: BigQuery Vector Search, Google Cloud Vector Search, Pinecone, Chroma, or FAISS
- SQL: Advanced query optimization for large-scale data warehouses
- Google Cloud Platform (Core focus): Gemini Enterprise, Vertex AI Agent Builder, BigQuery ML, Model Garden (Model development, fine-tuning, and deployment)
- Data Engineering & Orchestration: BigQuery, Dataflow, and Cloud Storage for data extraction, pipeline management, and indexing
- BI & Data Visualization Tools: Looker / Looker Studio, Tableau, or Power BI
- MarTech/AdTech Ecosystems
- Agile/Scrum Methodologies
- CI/CD & MLOps/LLMOps: Version control, automated deployment pipelines, LLM evaluation, and model monitoring tools
- Personal: Strong interpersonal skills, high energy and enthusiasm, integrity, and honesty; flexible, results-oriented, resourceful, structured problem-solving ability, deal effectively with difficult client situations, ability to prioritize.
- Leadership: Ability to gain credibility, motivate, and provide leadership; work with a diverse external customer base; maintain a positive attitude. Provide technical support and guidance to more junior team members, particularly for challenging modeling or analytical assignments.
- Operations: Commercial acumen and ability to manage multiple projects simultaneously. Perform tasks in a client-friendly manner while utilizing time and resources efficiently, proactively managing project scope.
- Technical: Ability to understand, translate, and communicate highly complex technical and mathematical concepts to non-technical business stakeholders.
This position is eligible to participate in an annual incentive program. Actual compensation offered to a candidate may vary based upon geographic location, work experience, education and/or skill levels.
- Medical, dental, vision
- tax-advantaged health care accounts
- financial and income protection benefits
- paid time off (PTO) and wellness time off.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
The position of Senior Consultant - Data Science & AI is within the TTEC Digital Analytics team. The Analytics group is responsible for Data Science, Advanced Analytics, Generative AI, and Engineering projects that include the design and validation of predictive models, building scalable machine learning pipelines, engineering autonomous agentic workflows, and leveraging enterprise Gen AI (LLMs, RAG architectures) to convert raw, unstructured client data into actionable insights that drive strategic business decision-making.
Key responsibilities
- Develop Machine Learning Models: End-to-end development, training, and deployment of production-grade predictive and prescriptive models (e.g., propensity modeling, customer segmentation, demand forecasting).
- Design & Deploy Generative AI Solutions: Architect, fine-tune, and evaluate enterprise Gen AI systems—including Retrieval-Augmented Generation (RAG) pipelines, custom LLM applications, and prompt engineering frameworks—to extract insights from unstructured data.
- Architect Agentic AI Systems: Design and deploy autonomous AI agents and multi-agent workflows capable of multi-step reasoning, tool utilization, and task automation to solve complex client business challenges.
- Deliver Advanced Analytics: Help clients maximize the efficiency of their strategic initiatives by building advanced analytics models that measure campaign performance, predict customer behavior, and maximize long-term ROI.
- Data Storytelling & Visualization: Synthesize complex algorithmic outcomes and machine learning metrics into compelling, executive-ready presentations and dashboards that clearly articulate business value and ROI.
- Develop and Maintain Analytics Workflows: Analyze raw client data and build optimized analytics datasets within cloud environments to support modeling and reporting.
- Lead workshops with external clients to uncover business objectives, map out data landscapes, and translate vague business problems into structured data science methodologies.
- Create solutions and design documentation for client data science architectures and ML pipelines.
- Work on projects independently as well as being part of a large team across multiple client engagements.
- Crosstrain Junior Data Scientists/Analysts or other team members with your area of expertise, providing technical leadership and code reviews.
- Further develop skills both on the job and through formal learning channels to stay ahead of AI/ML trends.
- Assist in pre-sales activities by scoping new client opportunities and providing accurate work/effort estimates.
Skills and Experience Requirements
- Post-Secondary Degree (or Diploma) related to Data Science, Statistics, Computer Science, Economics, Business Analytics, or an IT-related field.
- 5–8 years of total experience in Data Science, Advanced Analytics, or Management Consulting.
- 3+ years of experience in an external client-facing consulting or professional services capacity.
- 3+ years of application, model design, and deployment experience natively within a cloud environment (GCP preferred).
- 1–2+ years of hands-on experience architecting and deploying Generative AI systems (e.g., RAG pipelines, fine-tuned LLMs) and Agentic AI workflows (e.g., autonomous agents, tool-use integration).
- Demonstrated experience delivering both traditional predictive models and modern AI solutions to enterprise stakeholders.
- Google Cloud Certified Professional Data Engineer, Professional Machine Learning Engineer, or equivalent Generative AI/Cloud Certifications (Highly Preferred).
- Python Stack: pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow, Transformers
- Generative & Agentic AI Frameworks: LangChain, LlamaIndex, AutoGen/CrewAI, Prompt Engineering, RAG architectures, Multi-Agent Orchestration, Function Calling & Tool Integration
- Vector Databases & Search: BigQuery Vector Search, Google Cloud Vector Search, Pinecone, Chroma, or FAISS
- SQL: Advanced query optimization for large-scale data warehouses
- Google Cloud Platform (Core focus): Gemini Enterprise, Vertex AI Agent Builder, BigQuery ML, Model Garden (Model development, fine-tuning, and deployment)
- Data Engineering & Orchestration: BigQuery, Dataflow, and Cloud Storage for data extraction, pipeline management, and indexing
- BI & Data Visualization Tools: Looker / Looker Studio, Tableau, or Power BI
- MarTech/AdTech Ecosystems
- Agile/Scrum Methodologies
- CI/CD & MLOps/LLMOps: Version control, automated deployment pipelines, LLM evaluation, and model monitoring tools
- Personal: Strong interpersonal skills, high energy and enthusiasm, integrity, and honesty; flexible, results-oriented, resourceful, structured problem-solving ability, deal effectively with difficult client situations, ability to prioritize.
- Leadership: Ability to gain credibility, motivate, and provide leadership; work with a diverse external customer base; maintain a positive attitude. Provide technical support and guidance to more junior team members, particularly for challenging modeling or analytical assignments.
- Operations: Commercial acumen and ability to manage multiple projects simultaneously. Perform tasks in a client-friendly manner while utilizing time and resources efficiently, proactively managing project scope.
- Technical: Ability to understand, translate, and communicate highly complex technical and mathematical concepts to non-technical business stakeholders.
This position is eligible to participate in an annual incentive program. Actual compensation offered to a candidate may vary based upon geographic location, work experience, education and/or skill levels.
- Medical, dental, vision
- tax-advantaged health care accounts
- financial and income protection benefits
- paid time off (PTO) and wellness time off.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
See All 46 Data Science Manager Jobs in Texas
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Find Data Science Manager JobsData Science Manager Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
Data Science Manager Job Market in Texas
A snapshot from current Texas openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Retail
- Education
- Insurance
- Electronics & Hardware
What Texas Employers Look For
The qualifications that appear most often in data science manager jobs across Texas.
- Bachelor's or master's degree in data science, statistics, computer science, or a related field
- Five or more years of experience in data science or analytics with at least two in a leadership role
- Proficiency in Python, R, or SQL alongside experience with machine learning frameworks and cloud platforms
- Demonstrated ability to translate business problems into data-driven solutions and present findings to executives
- Experience managing cross-functional data science teams and owning the full model development lifecycle
- Familiarity with Texas industries such as energy, financial services, or healthcare data environments and their regulatory context
Data Science Manager Jobs in Texas: Frequently Asked Questions
How do you become a data science manager in Texas?
Becoming a data science manager in Texas typically requires a bachelor's degree in a quantitative field such as statistics, computer science, or engineering, with many employers preferring a master's degree. There is no state-issued license for this role in Texas. Most candidates build several years of hands-on data science work, develop leadership skills through project or team lead roles, and build a portfolio of end-to-end machine learning or analytics projects before stepping into management. Texas employers in energy, fintech, and healthcare often value domain knowledge alongside technical depth.
Which companies hire data science managers in Texas?
Employers hiring data science managers in Texas right now include Apple, Amazon, and Liberty Mutual Insurance, based on current listings on Migrate Mate as of September 2026. Texas's concentration of Fortune 500 headquarters, major energy firms, and a fast-growing fintech corridor in Dallas and Austin means consistent demand for experienced data science leadership across industries.
Which Texas cities have the most data science manager jobs?
Austin, Dallas, and Irving have the most data science manager openings in Texas. Houston's energy sector and large medical center drive demand there, Dallas draws volume from financial services headquarters and corporate tech hubs, and Austin's concentration of technology companies and high-growth startups makes it one of the most active markets for data science leadership in the state.
Are there remote data science manager jobs in Texas?
Yes, and more than most fields. About 72% of data science manager openings tied to Texas are remote or hybrid as of September 2026, reflecting the desk-based and highly portable nature of the work. Strategic planning, model review, and stakeholder reporting transfer well to remote settings, while roles tied to on-site data infrastructure or embedded industry teams such as energy operations tend to require more in-person presence.
How can I get hired as a data science manager in Texas with little or no experience?
The most realistic path is moving into management from a senior individual contributor role rather than applying directly to manager titles. Texas employers like Dell, USAA, and large hospital systems in the Texas Medical Center run associate data scientist programs and rotational analytics roles that build the cross-functional exposure managers need. Transitioning from a senior analyst, machine learning engineer, or data engineer role is common. A strong portfolio of end-to-end projects, a master's degree, and demonstrated mentorship or project leadership experience give candidates a concrete edge when competing for first-time manager openings.
Where can I find and apply to data science manager jobs in Texas?
You can find and apply to data science manager jobs in Texas on Migrate Mate, which lists current Texas openings updated regularly. Search for roles that match your experience level, preferred metro, and industry focus, then apply directly to the ones that fit.
See All 46 Data Science Manager Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
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