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# Senior Data Scientist

- **Company:** Sysco Corporation
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Cloud Database, Cloud Engineering, Code Review, Continuous Integration, Data as a Services, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Natural Language Processing, Performance Tuning, Recommender Systems, Software Tools, Tensorflow, Workflow Management Systems, Google Cloud, Feature Engineering, Pytorch, Prophet, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Deep Learning, Model Validation, Generative AI, Agentic-AI, Pyspark, Scikit Learn, Information Technology, Data Analytics, Xgboost, Performance Monitor, Machine Learning Operations
- **Published:** October 14, 2026
- **Apply:** https://wd5.myworkdaysite.com/recruiting/sysco/syscocareers/job/Sysco-LABS-----Sri-Lanka/Senior-Data-Scientist_R270255

## About the Role

* Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
 * 2-4 years of experience in Data Science, Machine Learning, AI, or related analytical disciplines.
 * Advanced expertise in Python and strong proficiency in SQL. * Strong knowledge of Machine Learning, Statistical Modelling, Deep Learning, Feature Engineering, and Model Evaluation techniques.
 * Experience building and deploying production-scale machine learning solutions.
 * Expertise in forecasting, time-series modelling, predictive analytics, anomaly detection, and advanced analytics techniques.
 * Hands-on experience with machine learning frameworks such as Scikit-learn, XGBoost, TensorFlow, PyTorch, Prophet, DeepAR, or equivalent.
 * Strong understanding of MLOps practices, including CI/CD, model deployment, monitoring, governance, and automation.
 * Experience with workflow orchestration and data engineering tools such as Airflow, Spark, PySpark, and ETL/ELT frameworks.
 * Experience with cloud platforms such as AWS, GCP, or Azure, including cloud-native ML and data services.
 * Strong knowledge of Generative AI technologies

## Description

* Work on end-to-end Data Science and Machine Learning projects, from problem definition and data exploration to model development, deployment, and performance monitoring.
 * Translate complex business problems into scalable analytical, machine learning, and AI solutions that deliver measurable business value.
 * Design and develop advanced machine learning models, including forecasting, predictive analytics, classification, recommendation systems, anomaly detection, and deep learning solutions.
 * Development and deployment of Generative AI applications using LLMs, RAG architectures, prompt engineering, Agentic AI frameworks, NLP techniques, and workflow orchestration tools.
 * Own model lifecycle management, including training, deployment, monitoring, explainability, validation, governance, and performance optimization.
 * Design and implement scalable cloud-based data science and AI solutions across AWS, GCP, Azure, or equivalent platforms.
 * Perform advanced statistical analysis, experimentation, feature engineering, forecasting, and root-cause analysis to enhance model accuracy and business outcomes.
 * Develop and maintain high-quality, reusable, and well-tested code following engineering best practices.
 * Technical troubleshooting and resolution of complex data, model, and pipeline issues in production environments.
 * Establish and promote best practices in data science, machine learning engineering, MLOps, and AI governance.
 * Collaborate with business stakeholders, product teams, engineering teams, and leadership to define roadmaps and technical strategies.
 * Mentor junior Data Scientists and contribute to knowledge sharing, onboarding, code reviews, and technical leadership within the team.
 * Communicate technical concepts, insights, risks, assumptions, and recommendations effectively to both technical and non-technical audiences.
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