Senior Data Scientist / Machine Learning Engineer - Bengaluru

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?          

• 6 to 10 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.