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About This Role
Williams is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity and expression, national origin, age, marital status, disability, veteran status, genetic information or any other basis protected under applicable discrimination law.
Do something that means something at Williams. This isn’t just a job \- it’s an opportunity to explore and discover your passion with coworkers who become friends and mentors who push you to be your best self in and out of the office. At Williams, we make clean energy happen. And you can too, so bring your energy to ours.
As a Business Data Scientist Lead, you will be responsible for driving the strategic design, development, regulatory compliance, testing, and implementation of enhancements to proprietary systems, while overseeing data visualization and advanced analytics initiatives. The role involves leading cross\-functional collaboration to deliver impactful projects that optimize revenue and ensure adherence to regulatory standards. Key responsibilities include identifying and analyzing business needs, translating them into actionable functional requirements, managing system modifications, and ensuring solutions align with organizational goals and compliance mandates. Additionally, you will provide leadership in process improvement, serve as the primary liaison between business and technical teams, and empowers the organization to make informed, data\-driven decisions through advanced analytics and reporting.
Your work will challenge you, and with our Core Values to guide you, you’ll quickly learn and grow with us.
Responsibilities/Expectations:
- Defines and champions the enterprise data science strategy, ensure alignment with business goals and emerging industry trends; Lead cross\-functional teams in the development and implementation of advanced analytics frameworks, standards, and best practices to drive value
- Architects, oversees, and optimizes complex analytical models, predictive and prescriptive solutions, and AI/ML platforms at scale. Drive the adoption of innovative technologies and methodologies
- Serves as an advisor to executive leadership on data\-driven opportunities and risks. Translate complex analytical findings into actionable business recommendations, influencing key strategic decisions
- Mentors and develops junior and senior data scientists, fostering a culture of continuous learning, collaboration, and high performance. Lead knowledge\-sharing initiatives and provide technical guidance on advanced statistical, AI, and ML solutions
- Provides 24/7 support to internal and external customers, serving as a liaison between customers and IT to validate and address product\-related issues. Accurately log, monitor, test, and resolve identified defects, ensuring all actions align with customer impact and regulatory compliance requirements
- Establishes and enforces data governance, model validation, and quality assurance protocols across analytics projects. Ensure compliance with industry standards, regulatory requirements, and ethical guidelines in data science practices
- Prepares and provides periodic reporting for stakeholders. Represent the organization at industry forums, conferences, and working groups; publish and present on innovative data science and analytics topics
- Other duties as assigned
Education/Years of Experience:
- Required: Bachelor’s degree in Data Science, Computer Science, Engineering, Mathematics, Business, or a related quantitative field and at minimum ten (10\) years of energy industry experience
Other Requirements:
- Demonstrates experience with operational, regulatory, systems, and accounting processes
- Transforms data into action\-oriented insights to support critical business decision\-making
- Designs and organizes user interfaces and data visualizations to improve accessibility and effectiveness of analysis
- Develops BI reports and dashboards using Power BI
- Utilizes SQL for data querying and Python or R for data manipulation and analysis
- Applied knowledge of databases (SQL, Oracle, Cube) and multi\-dimensional data structures
- Works with large datasets in a hybrid cloud environment, leveraging AI and ML methods to define requirements and set expectations
\#LI\-CT1About Houston (Williams Tower Location):
Our Houston office is located in the Williams Tower, just steps from the Houston Galleria on Post Oak Boulevard, an area with more than 700 retailers, fine dining and hotels within two square miles. We offer free onsite\-parking!
Houston is the fourth most populous city in the nation and greater Houston is the most ethnically diverse metropolitan area in the United States. Houston is a dynamic mix of imagination, talent and first\-class attractions that makes it a world\-class city!
If you love being outside, Houston rates first in total park acreage among U.S. cities with more than one million residents and offers a 300\-mile interconnected bikeway network spread over 500 square miles. Check out visithoustontexas to learn more!
Why Choose Williams?
We are committed to providing our employees with competitive compensation and benefits as part of your Total Rewards package to help protect your current and future physical, emotional, and financial health. We generally offer health benefit programs to our employees and their families that are competitive and flexible enough to meet your needs, and retirement benefits to allow you to invest now for financial security when you retire. With rich learning and development programming and a high internal mobility rate, you are not just applying to a job with Williams; you are embarking on an exciting career!
- Competitive compensation
- Annual incentive program
- Hybrid work model \- one work from home day each week for most office\-based roles
- Flexible work schedule for most field\-based roles
- 401(k) with company matching contribution and a fixed annual company contribution
- Comprehensive medical, dental, and vision benefits
- Generous company\-paid life insurance and disability benefits
- A consumer\-driven health plan option with the potential for a generous company contribution to a Health Savings Account
- Healthcare and Dependent Care Flexible Spending Accounts
- Paid time off, including floating and company holidays
- Employee stock purchase plan
- Robust employee learning and development
- High internal mobility (we promote from within)
- Parental leave (we provide up to 6 weeks for each parent)
- Fertility coverage and adoption benefits
- Domestic partner benefits
- Educational reimbursement
- Non\-profit donation matching contributions and time off to volunteer
- Employee resource groups
- Employee assistance programs
- Technology to make our work more productive and collaborative
- Regular employee engagement surveys and feedback processes
Williams has a long history of making a significant difference in the communities where we live and work, and we strive to cultivate an environment of employee inclusion, innovation and passion that values all voices and opinions. We help each other succeed and great things happen when people from a diverse set of backgrounds come together. Together, we make clean energy happen.
*Eligibility and benefits are governed by the terms of the applicable plan or program document which can be amended or terminated at any time.*
For more information, please visit Total Rewards \| Williams Companies.
Education Requirements:
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Bachelors (Required)Skill Requirements:
Competency Requirements:
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Williams, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Williams AI Hiring
Williams has 1 open AI role right now. They're hiring across Data Scientist. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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