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About This Role
Westlake offers you the potential to enrich your work life and career experience in an entrepreneurial environment. We work together to enhance peoples' lives through our products and presence in the communities in which we operate.
SUMMARY
Westlake Corporation AI \& Digital Office operates at the heart of the strategic direction of our enterprise. The Digital Office team drives digital transformation across the enterprise’s entire portfolio to achieve growth through leapfrogging customer experience and operational excellence.
As a member of the AI \& Digital Office, the Data Scientist I \- Digital will work with Data Scientists, Data Analysts, business partners, and functional subject matter experts to design and build AI agents and agent\-enabled workflows that align to existing business objectives and deliver measurable value.
As a Data Scientist at Westlake Corporation, they will provide hands\-on engineering support for AI \& Digital Office initiatives. This includes developing and testing AI agents in Python, integrating tools and enterprise data sources via APIs, documenting and debugging agent behavior, and contributing to deployment and operationalization on Microsoft Azure. They will collaborate closely with technical and non\-technical stakeholders to translate business needs into reliable, secure, and maintainable agent solutions.
ESSENTIAL DUTIES AND RESPONSIBILITIES
General Description
- Receives instruction, direction, and guidance from senior data scientists, MLOps engineer, and project team members
- Develop and implement AI workflows and applications as part of existing project milestones
- Investigate approaches to solve novel data science problems with other technical team members
- Learn and use data science and software development best practices in projects
Data Management and Analysis
- Work with senior team members to collect, clean, and preprocess data for analysis.
- Assist in the development and implementation of machine learning algorithms and statistical models.
- Help maintain data pipelines and ensure the integrity and quality of data used in analyses.
- Support the team in the interpretation of data and communication of findings to non\-technical stakeholders.
Machine Learning and Model Development
- With proper oversight, participate in the design and testing of predictive models and LLM custom workflows.
- Assist senior team members in fine\-tuning models to improve accuracy and performance.
- Help evaluate new data sources for model enhancement and feature engineering.
- Contribute to the deployment of machine learning models into production environments.
Special Projects
- Work with senior team members to explore and integrate new technologies and methodologies in AI and data science.
- Assist in research and assessment of the latest trends and developments in machine learning and AI.
- Participate in the setup and evaluation of new analytical tools and software for the team.
QUALIFICATIONS
- Proficient in programming and markup languages such as Python, SQL, and familiarity with machine learning tools and AI platforms.
- Ability to present ideas in user\-friendly language to a non\-technical audience.
- Ability to conduct research into data science operational issues, standards, and products as required.
- Analytical and problem\-solving abilities, with a strong foundation in statistics, machine learning, LLMS, software development, and AI agent development.
- Hands\-on experience developing and testing AI agents using Python, including prompt/tool orchestration, API integration, and basic evaluation/debugging workflows.
- Proficiency with modern developer tooling for agent development, including VS Code, Git, and agent\-coding platforms/frameworks, and familiarity with deploying or integrating solutions on Microsoft Azure.
- Ability to effectively prioritize and execute tasks in a high\-pressure environment.
EDUCATION and/or EXPERIENCE
- Undergraduate degree in Computer Science, Computer Engineering, or equivalent
- Experience developing business solutions with tools \& frameworks such as Pandas, Scikit\-learn, TensorFlow, or PyTorch is a plus.
- Experience with modern cloud application technical architecture
- Familiarity with version control systems such as Git and development environments like Jupyter Notebooks or VS Code.
PHYSICAL DEMANDS
While performing the duties of this job, the employee is regularly required to sit. The employee frequently is required to walk; use hands to touch, handle, or feel; reach with hands and arms; and talk or hear. The employee is occasionally required to stand. The employee must regularly lift and/or move up to 10 pounds, frequently lift and/or move up to 25 pounds. Any lifting in excess of 25 pounds requires assistance from another employee. Specific vision abilities required by this job include close vision, distance vision, color vision, and ability to adjust focus. The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
WORK ENVIRONMENT
The noise level in the work environment is usually moderate as normally based in an office. Some of the work may be required in the operating units which can require usage of required PPE including safety glasses, hearing protection, etc. May also result in exposure to outside elements and may require usage of stairs and elevators. Travel up to *20*% including air travel or auto travel.
Westlake is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to any characteristics protected by applicable legislation.
If you are an active Westlake employee (or an employee of any Westlake affiliates), please do not apply here. You will apply via the Jobs Hub application in Workday.
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Westlake Corporation, 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Westlake Corporation AI Hiring
Westlake Corporation has 1 open AI role right now. They're hiring across Data Scientist. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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