- Company Name
- Toast
- Job Title
- Principal Data Scientist
- Job Description
-
**Job Title:** Principal Data Scientist
**Role Summary:**
Lead design, development, and production of scalable machine‑learning systems that drive product performance, operational efficiency, and customer insights for a technology company serving restaurants and hospitality businesses. Own the full ML lifecycle, set technical standards, influence product roadmap, and mentor the data science team.
**Expectations:**
- 10+ years of data science experience with measurable production impact.
- Proven technical leadership and mentorship of junior data scientists.
- Deep knowledge of statistical modeling, ML algorithms (tree‑based, time‑series, deep learning), and model evaluation.
- Ability to translate ambiguous business problems into scoped ML solutions.
- Technical proficiency in Python and SQL; experience with scikit‑learn, PyTorch, TensorFlow.
- Strong software engineering fundamentals (modular design, version control, testing, CI/CD).
- Hands‑on cloud experience (AWS preferred: SageMaker, Athena, Glue, DynamoDB, Bedrock).
- Excellent communication and stakeholder influence skills; solid business acumen.
**Key Responsibilities:**
- Own end‑to‑end ML projects: problem framing, data exploration, modeling, deployment, monitoring.
- Design and implement advanced ML and statistical models for menu recommendation, demand forecasting, offer targeting, guest personalization.
- Collaborate with engineering, product, and business stakeholders to define scope, metrics, and integration strategy.
- Guide architectural decisions, set modeling standards, champion best practices for experimentation, validation, and productionization.
- Mentor and uplift the data science team through design reviews, feedback, and knowledge sharing.
- Identify high‑impact data science opportunities and lead cross‑functional initiatives.
- Lead experimentation (A/B testing), causal inference, and real‑time decision systems.
- Maintain ML Ops pipelines, monitoring, drift detection, retraining, and explainability.
**Required Skills:**
- Advanced statistical and ML modeling (tree‑based, time‑series, deep learning).
- Distributed data processing and real‑time inference (Spark, Flink, Dask).
- ML Ops frameworks (SageMaker, Kubeflow, MLflow).
- Python, SQL, Git, Docker, Kubernetes.
- Cloud (AWS) services: SageMaker, Athena, Glue, DynamoDB, Bedrock.
- Experimentation design, A/B testing, causal inference techniques.
- Strong communication across technical and non‑technical audiences.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Statistics, or related STEM field (advanced degree strongly preferred).
- Any relevant certifications (e.g., AWS Certified Machine Learning – Specialty) are a plus.