Principal Data Scientist

Change the world. Love your job.

Roles and duties:

• Stakeholder engagement:
• Work collaboratively and strategically with stakeholder groups to achieve 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

Qualifications

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).