Compunnel, Inc.
Client is seeking a talented Data Scientist to join our Enterprise Advanced Data Analytics team and contribute to enterprise-level complex business solutions. In this role, you will be at the forefront of developing cutting-edge AI and machine learning enterprise-grade solutions. The successful candidate will design, develop, and deploy robust machine learning algorithms to solve complex business problems across supply chain, factory operations, workforce, and logistics. You will collaborate closely with stakeholders to understand business requirements and translate them into actionable solutions.
Key Responsibilities
Own end-to-end solutions: design, develop, and deploy robust machine learning algorithms, including large language models. Collaborate with stakeholders to translate business requirements into actionable solutions. Conduct data discovery and analysis by cleaning, preprocessing, and transforming raw data into usable formats. Ensure data quality and integrity throughout the analytics process. Produce visualizations and insights for both technical and non‑technical audiences. Develop and train large language models using deep learning techniques such as transformers and recurrent neural networks. Required Qualifications
Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field. 6+ years of professional experience as a Data Scientist, with a focus on generative AI and machine learning. Strong understanding of large language models (LLMs) and their applications in AI. Experience with Agent + RAG systems and their application in data science and AI. Proficiency in Python/R programming languages and machine learning libraries such as LangChain, scikit-learn, TensorFlow, and PyTorch. Experience with data visualization tools such as Tableau, Power BI, and others. Familiarity with cloud‑based services and solutions such as AWS, Azure, and Dataiku. Preferred Qualifications (if any)
Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. Experience working with large datasets and high‑performance computing environments. Strong understanding of data modeling, statistical inference, and advanced machine learning techniques. Excellent communication and presentation skills. Experience with Docker and containerization. Familiarity with DevOps practices and CI/CD pipelines. Experience with cloud‑based data warehousing and big data processing (e.g., AWS Glue, Apache Spark). Certifications (if any)
Relevant certifications in AI/ML, cloud computing, or data science are a plus.
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Own end-to-end solutions: design, develop, and deploy robust machine learning algorithms, including large language models. Collaborate with stakeholders to translate business requirements into actionable solutions. Conduct data discovery and analysis by cleaning, preprocessing, and transforming raw data into usable formats. Ensure data quality and integrity throughout the analytics process. Produce visualizations and insights for both technical and non‑technical audiences. Develop and train large language models using deep learning techniques such as transformers and recurrent neural networks. Required Qualifications
Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field. 6+ years of professional experience as a Data Scientist, with a focus on generative AI and machine learning. Strong understanding of large language models (LLMs) and their applications in AI. Experience with Agent + RAG systems and their application in data science and AI. Proficiency in Python/R programming languages and machine learning libraries such as LangChain, scikit-learn, TensorFlow, and PyTorch. Experience with data visualization tools such as Tableau, Power BI, and others. Familiarity with cloud‑based services and solutions such as AWS, Azure, and Dataiku. Preferred Qualifications (if any)
Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. Experience working with large datasets and high‑performance computing environments. Strong understanding of data modeling, statistical inference, and advanced machine learning techniques. Excellent communication and presentation skills. Experience with Docker and containerization. Familiarity with DevOps practices and CI/CD pipelines. Experience with cloud‑based data warehousing and big data processing (e.g., AWS Glue, Apache Spark). Certifications (if any)
Relevant certifications in AI/ML, cloud computing, or data science are a plus.
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