Principal Data Scientist

Change the world. Love your job.

Texas Instruments is seeking experienced Data Scientist to join our team. As the Data Scientist in TI's Demand Analytics team, you will play a pivotal role in shaping and executing our demand planning and inventory buffer strategies for the company. You will be working side by side with a team of highly technical professionals that will consists of application developers, system architects, data scientists and data engineers. This role will be responsible for solving complex business problems through innovative solutions that deliver tangible business value. This position requires a technical leader with a strong technical background in AI/ML, simulation solutions, strategic thinking, and a passion for innovation through data.  This team is responsible for:  portfolio management for demand forecasting algorithms, generation of inventory buffer targets, segmentation of TI's products and simulation/validation frameworks, defining specs/reference architectures to best achieve business outcomes and ensuring security and interoperability between capabilities.

Roles and duties:

• Stakeholder engagement:
  • Work collaboratively and strategically with stakeholder groups to achieve TI business strategy and goals
  • Communicate complex technical concepts and influence final business outcomes with stakeholders effectively
  • Partner with cross-functional teams to identify and prioritize actionable, high-impact insights across a variety of core business areas
• Technology and platforms:
  • Build simple, scalable and modular technology stacks using modern technologies and software engineering principles.
  • Simulate real world scenarios with various models and approaches to determine best fit of algorithms by varying the inputs across hundreds to thousands of variables
  • Research, experiment and implement new approaches and models that flex with the business strategy transformations
  • Leads data acquisition and engineering efforts
  • Develops and applies machine learning, AI and data engineering framework
  • Solutions, writes and debugs code for complex development projects
  • Oversees, evaluates and determines the best modeling techniques for various scenarios letting the data drive the conversation

Minimum requirements:

• MS or PhD in a quantitative field (e.g., Computer Science, Statistics, Engineering, Mathematics) or equivalent practical experience.
• 8+ years of professional experience in data science or a related role.
• 5+ years of hands-on experience developing and deploying time series forecasting models in a professional setting.
• Demonstrated experience in the supply chain domain (e.g., Semiconductor, Retail, CPG, Pharmaceutical), with a deep understanding of concepts like demand forecasting, S&OP, or inventory management.
• Expert-level proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Statsmodels) and forecasting packages (e.g., Prophet, PyTorch Forecasting).
• Proven experience taking machine learning models from prototype to production, including knowledge of CI/CD and model monitoring.

Preferred qualifications:

• Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes).
• Practical experience with cloud data science platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform).
• Familiarity with advanced forecasting techniques such as probabilistic forecasting, causal inference, or using Transformers for time series.
• Experience applying NLP to extract features from unstructured text to enhance forecasting models.
• Strong SQL skills and experience working with large-scale data warehousing solutions (e.g., Snowflake, BigQuery, Redshift).