Website Chick-fil-A, Inc.
Chick-fil-A, Inc.
Company : Chick-fil-A, Inc.
How We Work At Chick-fil-A:
Chick-fil-A, Inc. ('Chick-fil-A' or 'the Company') Staff members play a vital role in achieving our strategic goals by developing their skills, fostering inclusive teamwork, and embracing innovation. All Staff are expected to contribute to a compelling future by inspiring and motivating those around them. Growth and development are essential at Chick-fil-A. We want Staff to seek new perspectives and adopt new methods to drive continuous improvement and adaptation to evolving business needs. Lastly, we ask Staff to seek wisdom, expect the best, accept responsibility, respond with courage, and think others first.
Our Flexible Futures Model offers a healthy mix of working in person and virtually, strengthening key elements of the Chick-fil-A culture by fostering collaboration and community.
Overview:
The Principal Data Scientist is a senior individual contributor responsible for defining and leading advanced analytical approaches to complex and ambiguous business problems. This role shapes modeling strategy and technical direction for high-impact analytics initiatives, primarily supporting Restaurant Development while influencing broader enterprise analytics capabilities.
The role is responsible not only for building models but also for framing analytical problems, defining modeling approaches, and establishing reusable analytical patterns that other data scientists can build upon. The Principal Data Scientist works closely with cross‑functional partners including business stakeholders, data engineers, analysts, and product teams to translate large and complex problems into actionable insights.
The ideal candidate combines deep expertise in statistical modeling, machine learning, and data analysis with strong problem‑solving and communication skills. This role contributes to high‑impact analytical initiatives and helps elevate analytical practices within project teams while remaining primarily focused on hands‑on modeling and technical delivery.
Responsibilities:
Responsibilities – Advanced Analytics & Modeling
- Define modeling approaches and analytical frameworks for complex or ambiguous problems where established solutions do not yet exist.
- Apply predictive modeling, forecasting, classification, clustering, and other advanced analytics techniques to large datasets.
- Explore and integrate diverse data sources including transactional, behavioral, demographic, geospatial, and operational data.
- Build and validate models using best practices for feature engineering, experimentation, and model evaluation.
- Translate analytical findings into clear insights and recommendations for business stakeholders.
Responsibilities – Technical Contribution
- Develop production‑ready analytical models and support their integration into data platforms and business workflows.
- Identify modeling risks early, including bias, data limitations, drift, and statistical assumptions that could impact model validity.
- Collaborate with data engineers and analytics teams to ensure data quality, model reliability, and scalable implementation.
- Contribute to the development of reusable analytical frameworks, code libraries, and modeling workflows.
- Support the evaluation and selection of appropriate analytical techniques and tools for specific use cases.
- Document analytical approaches, model assumptions, and results to ensure reproducibility and transparency.
Responsibilities – Cross‑Functional Collaboration
- Partner with business stakeholders to understand analytical needs and translate business questions into data science solutions.
- Communicate complex analytical results in a clear and concise manner to both technical and non‑technical audiences.
- Contribute to cross‑functional analytics initiatives and collaborate with engineering, product, and analytics teams.
- Provide guidance and knowledge sharing on modeling approaches and analytical methods within project teams.
Required Qualifications (Knowledge, Skills, & Abilities):
- 6+ years of experience in data science, machine learning, or advanced analytics.
- Strong understanding of statistical modeling, machine learning algorithms, and predictive analytics.
- Proficiency in Python and SQL for data analysis and model development.
- Experience working with large datasets and modern data platforms.
- Strong problem‑solving, analytical thinking, and communication skills.
Preferred Qualifications (Knowledge, Skills, & Abilities):
- 8+ years of experience in data science, machine learning, or advanced analytics.
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar tools.
- Experience with distributed data processing frameworks such as Spark.
- Exposure to cloud‑based analytics platforms such as AWS, Azure, or GCP.
- Experience applying advanced analytics techniques such as geospatial analysis, optimization, or network modeling.
Required Years of Experience:
6
Preferred Years of Experience:
8
Travel Requirements:
10%
Required Level of Education:
Bachelor's Degree
Preferred Level of Education:
Master's Degree
Major/Concentration:
Statistics, Computer Science, Applied Mathematics, Data Science, Engineering, or a related quantitative field.
Relocation Assistance Provided:
No