Data Scientist

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.

Location: Bengaluru, India

Location Type: Hybrid This role offers a balanced arrangement, with the expectation of working 3 days a week in our local office and the flexibility to work from home for the remaining days. It is essential that you reside within a reasonable commuting distance of the office location for the in-office workdays.