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About This Role
Overview:
WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next\-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
Responsibilities:
THE ROLE:
We are seeking a highly skilled Data Scientist to lead initiatives in advanced analytics, predictive modeling, and generative AI for our Sales Operations org. This role combines statistical expertise, machine learning, and cutting\-edge generative models to deliver actionable insights that drive strategic decisions across multiple business areas. As part of our team, you will collaborate with cross\-functional teams to optimize revenue, improve operations, and empower sales and marketing professionals by leveraging data\-driven solutions and AI to minimize manual tasks, enabling them to focus more efficiently on selling products. THE PERSON:
The person we are looking for should have passion in data science. He/she has strong SQL and programming skills (Python is preferred) and has a good understanding of statistics and machine learning algorithms. KEY RESPONSIBILITIES:* Predictive Modeling \& Machine Learning:
- + Develop and deploy predictive models using machine learning algorithms (e.g., XGBoost, Random Forest) to address business challenges such as customer segmentation and revenue forecast.
+ Build end\-to\-end ML pipelines from data ingestion to model deployment, ensuring scalability and reliability.
- Time\-Series Forecasting:
- + Create robust time\-series forecasting models for revenue, demand, and operational metrics using techniques like ARIMA, Prophet, Bayesian methods, and hybrid approaches.
+ Partner with finance, sales, and operations teams to integrate forecasts into strategic planning and decision\-making processes.
- Generative AI \& Advanced Analytics:
- + Design and implement generative AI solutions (e.g., LLMs, GANs) for business applications such as information retrieval, workflow automation, and AI\-driven decision systems.
Data Pipeline \& Infrastructure:
- + Collaborate with data engineering teams to design and maintain scalable data pipelines using tools like Airflow, KNIME, or custom shell scripting.
+ Leverage big data frameworks (e.g., Snowflake, Hadoop, Spark) for efficient data processing and storage.
- Exploratory Data Analysis \& Visualization:
- + Perform exploratory data analysis to uncover patterns and insights from structured and unstructured data sources.
+ Develop visualizations using tools such as Plotly, Matplotlib and Seaborn to communicate findings effectively to stakeholders.
- Collaboration \& Impact Measurement:
- + Work closely with business leaders, data engineers, and BI teams to align on business needs and deliver impactful solutions.
+ Monitor model performance post\-deployment and iterate on models to ensure continued business impact.
- Continuous Learning \& Innovation:
- + Stay updated on emerging technologies in AI, machine learning, and generative models.
+ Experiment with new tools and techniques to enhance team capabilities and drive innovation.
QUALIFICATIONS:* Education:
Bachelor’s degree (Master’s preferred) in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field.
- Technical Skills:
- + Proficiency in Python, SQL, and other programming languages.
+ Strong understanding of machine learning algorithms and statistical techniques.
+ Hands\-on experience with ML libraries (e.g., Scikit\-learn, TensorFlow, PyTorch) and frameworks for generative AI.
+ Familiarity with time\-series forecasting methods and tools.
+ Experience working with big data technologies (e.g., Snowflake, Hadoop, Spark).
+ Basic knowledge of UNIX shell scripting (Bash, Zsh).
- Experience:
- + Minimum of 3\+ years of experience in data science or related fields.
+ Demonstrated ability to deliver end\-to\-end ML projects from start to finish.
+ Experience with version control tools like Git and GitHub.
- Business Acumen:
- + Ability to translate technical insights into actionable business strategies.
+ Strong communication skills to effectively collaborate with cross\-functional teams.
This role is not eligible for visa sponsorship.
LOCATION:
Austin, TX
\#LI\-RF1
\#LI\-HYBRID *AMD does not accept unsolicited resumes from headhunters, recruitment agencies or fee based recruitment services. AMD and its subsidiaries are equal opportunity employers and will consider all applicants without regard to race, marital status, sex, age, color, religion, national origin, veteran status, disability or any other characteristic protected by law. EOE/MFDV*
Qualifications:
*Benefits offered are described:* AMD benefits at a glance. *AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee\-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third\-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.* *AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available* *here.* *This posting is for an existing vacancy.*
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At AMD, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AMD AI Hiring
AMD has 21 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span San Diego, CA, US, Austin, TX, US, San Jose, CA, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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