Data Scientist (Sales Operations)

Advanced Micro Devices, Inc•Austin, TX

2h•Hybrid

About The Position

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. What you do at AMD changes everything At AMD, we push the boundaries of what is possible. We believe in changing the world for the better by driving innovation in high-performance computing, graphics, and visualization technologies – building blocks for gaming, Immersive platforms, and the data center. Developing great technology takes more than talent: it takes amazing people who understand collaboration, respect, and who will go the “extra mile” to achieve unthinkable results. It takes people who have the passion and desire to disrupt the status quo, push boundaries, deliver innovation, and change the world. If you have this type of passion, we invite you to take a look at the opportunities available to come join our team. THE ROLE: We are seeking a highly skilled Data Scientist to lead initiatives in advanced analytics, predictive modeling, and generative AI. 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 I am 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.

Requirements

Bachelor’s degree (Master’s preferred) in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field.
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).
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.
Ability to translate technical insights into actionable business strategies.
Strong communication skills to effectively collaborate with cross-functional teams.

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.