Job Description:
We Are Omnissa!
Omnissa is the first AI-driven digital work platform, built to support flexible, secure, work-from anywhere experiences. We integrate industry-leading solutions—including Unified Endpoint Management, Virtual Apps and Desktops, Digital Employee Experience, and Security & Compliance - into a seamless, autonomous workspace that adapts to how people work. Our platform boosts employee engagement while optimizing IT operations, security, and cost.
Guided by our Core Values - Act in Alignment, Build Trust, Foster Inclusiveness, Drive Efficiency, and Maximize Customer Value - we’re growing rapidly and committed to delivering meaningful impact. If you're passionate about shaping the future of work, we’d love to hear from you.
The Opportunity
The Data Operations team within Omnissa’s Product organization is responsible for building AI/ML-powered data products and agentic platforms. These solutions enable internal teams (Product, Marketing, Sales) and customers with the data, insights, and tools needed to drive adoption, bookings, and revenue.
As a Lead Data Scientist, you will play a key role in advancing our AI/ML and analytics capabilities. This is a highly hands-on role requiring strong technical depth and business acumen. You will partner closely with cross-functional teams to solve complex problems, develop scalable AI solutions, and operationalize models and intelligent agents across the organization.
Key Responsibilities
Design and develop advanced machine learning models using structured, semi-structured, and unstructured data
Build and scale end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring
Lead the development of AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents
Collaborate with engineering teams to deploy and operationalize models and AI systems in production environments
Optimize models and pipelines for performance, scalability, reliability, and cost efficiency
Perform deep-dive analyses to uncover actionable insights, trends, and opportunities
Translate analytical findings into clear recommendations that influence product strategy and go-to-market decisions
Drive adoption of best practices in machine learning engineering, experimentation, and model lifecycle management
What will you bring to Omnissa?
7 to 13 years of experience in Data Science, Machine Learning, or related roles
Strong analytical thinking and problem-solving skills, with attention to detail
Hands-on experience in one or more of the following areas (experience across multiple domains preferred):
Supervised and unsupervised learning
Classification and regression
Clustering and segmentation
Time-series analysis
Anomaly detection
Recommendation Systems
Natural Language Processing (NLP) / Information Retrieval
Strong programming expertise in Python and SQL
Experience with ML and AI frameworks such as Scikit-learn, Pandas, NumPy, SciPy, Hugging Face, and transformer-based models
Experience building GenAI applications using LLMs, RAG architectures, vector databases, agent orchestration frameworks (such as LangChain, LangGraph) and production deployment patterns
Experience building or contributing to evaluation and observability frameworks for LLM and agentic systems (e.g., LangSmith, RAGAS, custom evaluation pipelines, tracing, monitoring, or similar tools)
Hands-on experience deploying, productionizing, and monitoring ML models and AI agentic systems on AWS; familiarity with Azure or GCP is a plus
Ability to translate business problems into AI/ML solutions, define success metrics and measure business impact of deployed solutions
Excellent communication skills, with the ability to present complex analyses to both technical and non-technical audiences
Preferred
Bachelor’s or Master’s degree in Computer Science, Data Science, or related field
Understanding of SaaS business metrics such as ARR, NRR, GRR, churn, retention, and ACV
Experience working in Agile environments
Familiarity with Salesforce or go-to-market (GTM) analytics systems
Knowledge of data governance, schema design, and scalable data infrastructure
Experience analyzing product usage data and driving product adoption, engagement, retention, and monetization strategies
Ability to thrive in a fast-paced environment and manage multiple priorities independently
Experience working with any large-scale data platforms and distributed processing technologies such as Spark, Databricks, Snowflake, Trino, Presto, etc.
Skills Required
- 7 to 13 years of experience in Data Science or Machine Learning
- Hands-on experience designing and developing ML models using structured and unstructured data
- Strong programming expertise in Python and SQL
- Experience with ML/AI frameworks: Scikit-learn, Pandas, NumPy, SciPy, Hugging Face, transformer-based models
- Experience building GenAI applications using LLMs, RAG architectures, and vector databases
- Experience with agent orchestration frameworks (e.g., LangChain, LangGraph) and agentic systems
- Experience building evaluation and observability frameworks for LLMs/agent systems (e.g., LangSmith, RAGAS, custom pipelines)
- Hands-on experience deploying, productionizing, and monitoring ML models and AI systems on AWS
- Ability to translate business problems into AI/ML solutions and define success metrics
- Excellent communication skills to present analyses to technical and non-technical audiences
- Familiarity with Azure or GCP
- Bachelor's or Master's degree in CS, Data Science, or related field
- Understanding of SaaS metrics (ARR, NRR, churn, retention, ACV) and GTM analytics (Salesforce familiarity)
- Experience with large-scale data platforms and distributed processing (Spark, Databricks, Snowflake, Trino, Presto)
- Knowledge of data governance, schema design, and scalable data infrastructure
Omnissa Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Omnissa and has not been reviewed or approved by Omnissa.
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Healthcare Strength — Healthcare offerings include comprehensive medical, dental, and vision coverage, with wellness options referenced across materials. Health plans are characterized as decent to strong within a standard tech package.
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Retirement Support — A 401(k) with company match is part of the core package and is specifically highlighted as a valued benefit in U.S. materials. Retirement support is presented as a stable element of total rewards.
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Leave & Time Off Breadth — Vacation and PTO are highlighted positively, with generous paid time off and holidays noted in public benefits descriptions. Time-off programs are portrayed as supportive of work-life balance.
Omnissa Insights
What We Do
Omnissa is the digital work platform leader, trusted by thousands of organizations worldwide as the former VMware End-User Computing business. We make digital work, work – for businesses and their people. No painful IT processes or productivity trade-offs. Instead, a seamlessly delivered digital employee experience that simplifies work. Our comprehensive digital work platform enables IT teams to provide secure, personalized experiences for every employee, on any device. Omnissa unifies, automates, and efficiently scales the digital workspace. By empowering employees to do their best work, anywhere, we help workforces everywhere unlock exponential business value. All is made possible with the Omnissa™ Platform, the first AI-driven digital work platform for smart, seamless, and secure work experiences from anywhere. It integrates multiple industry-leading solutions across Unified Endpoint Management, Virtual Desktops and Apps, Digital Employee Experience, and Security and Compliance. By continuously adapting to users’ work styles, Omnissa optimizes user experience, security, IT operations and costs.
