Job Description
Omnissa seeks a Staff Data Scientist to lead AI/ML development. You'll design, implement, and optimize ML models, work with LLMs and RAG, and mentor junior data scientists. 15+ years of hands-on experience in machine learning, deep learning, and data analysis are essential.
Good To Have:
- PyTorch
- AWS/Azure
- Prompt Engineering
- Ethical AI
Must Have:
- Machine Learning
- Deep Learning
- Data Analysis
- LLMs Experience
Perks:
- Mentorship
- Innovation
About the job
Job Summary
Omnissa, the former VMware End User Computing (EUC) business, is a company with a vision to deliver an AI driven autonomous Digital workspace that enables smart, seamless and secure work experiences from anywhere. Powered by state-of-the-Art ML Solutions, the Omnissa Platform is self-configuring, self-healing, and self-securing and uniquely integrates multiple industry-leading solutions including Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance. It continuously adapts to the way people work, delivering personalized and engaging employee experiences, while optimizing security, IT operations and costs. You will be part of the “Data Sciences and Machine Learning” Team– the innovation and development engine that builds the AI features available in Omnissa’s Products . As a Staff Data Scientist, you will lead and innovate within the data science team to drive significant advancements in our ML/data analytics capabilities. This is a hands-on role and you will be expected to develop AI/ML based solutions, work closely with cross-functional teams to solve complex business problems, and contribute to the company’s vision through advanced analytics, machine learning, and artificial intelligence
Key Responsibilities
Omnissa, the former VMware End User Computing (EUC) business, is a company with a vision to deliver an AI driven autonomous Digital workspace that enables smart, seamless and secure work experiences from anywhere. Powered by state-of-the-Art ML Solutions, the Omnissa Platform is self-configuring, self-healing, and self-securing and uniquely integrates multiple industry-leading solutions including Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance. It continuously adapts to the way people work, delivering personalized and engaging employee experiences, while optimizing security, IT operations and costs. You will be part of the “Data Sciences and Machine Learning” Team– the innovation and development engine that builds the AI features available in Omnissa’s Products . As a Staff Data Scientist, you will lead and innovate within the data science team to drive significant advancements in our ML/data analytics capabilities. This is a hands-on role and you will be expected to develop AI/ML based solutions, work closely with cross-functional teams to solve complex business problems, and contribute to the company’s vision through advanced analytics, machine learning, and artificial intelligence
Key Responsibilities
- Lead the design and development of advanced data science and machine learning analytics models using structured, unstructured and semi-structured data.
- Work with engineers to design and implement machine learning pipelines, covering all stages from data ingestion and feature extraction to training, testing, validation, inference, and continuous learning in production systems.
- Leverage key technologies and state-of-the-art tools necessary for exploring/querying data, visualization, and advanced analytics - distribution of key attributes, relationships between attributes, feature engineering, and statistical analyses.
- Be an expert in and lead the development of Large Language Models (LLMs) and Retrieval-augmented generation (RAG) based Solutions.
- Develop and optimize algorithms for training and fine-tuning LLMs, improving their performance, accuracy, efficiency, and scalability.
- Design and implement prompt engineering strategies to optimize the performance of LLM based applications.
- Participate in design/code reviews, and knowledge-sharing sessions to maintain high standards of product development excellence.
- Mentors and guide junior data scientists and engineers, fostering a culture of continuous learning and professional growth.
- Collaborate with cross-functional teams to integrate AI/ML enabled features into existing products.
- Track advances in industry and academia to stay up to date with the latest research and algorithms in the field of machine learning and AI, and drive innovation by incorporating relevant advancements into ongoing projects.
- Actively contribute to the body of thought leadership and intellectual property (IP) best practices by actively participating in external conferences.
- Masters or Doctorate Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field.
- 15+ years of hands-on experience in using statistical machine learning, deep learning, data mining, data analysis, information retrieval, optimization algorithms.
- 2+ years of hands-on experience working with large language models (LLMs).
- Proficient in Python and working knowledge of at least one other programming languages such as Java, Scala, or C++.
- Extensive experience with frameworks and libraries such as PyTorch, Numpy, Pandas, SciPy, Scikit-Learn, LangChain and Hugging Face Transformers.
- Proficiency in SQL and experience with big data technologies such as Hadoop, Spark, or equivalent.
- Demonstrated expertise in training, fine-tuning, and deploying machine learning models, particularly LLMs.
- Proficiency in prompt engineering and retrieval-augmented generation (RAG) techniques.
- Familiarity with cloud platforms and Tools such as AWS, Azure, or Google Cloud for development, deploying and scaling ML models.
- Excellent problem-solving skills, with a track record of successfully addressing complex technical challenges.
- Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams.
- Demonstrated commitment to ethical AI practices and data privacy/security regulations.
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