Data Scientist

About the role:

Samsara Technologies India Private Limited is seeking a Data Scientist to advance our data-driven efforts in sales, marketing, and customer success. As part of the Data Science and AI team in Revenue Operations, this role focuses on designing and delivering predictive models, refining AI-based scripts to validate, augment, and test the accuracy of business critical data, and establishing the quality measurement, lineage, and input pipelines of that data. You’ll collaborate closely with stakeholders in BizTech, Marketing, Sales, and Operations to unlock insights, automate processes, and enhance the operational efficiency of the sales organization. 

This hybrid position requires 3 days per week in our Bangalore office and 2 days working remotely. This position requires working hours in IST. Relocation assistance will not be provided for this role.

You should apply if: 

• You want to impact the industries that run our world: Your efforts will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
• You have an innate curiosity about how businesses work: One day you’ll meet with someone in waste management and the next you may be learning about the inner workings of a food distribution center. Our top sales team members seek to learn the ins and outs of the businesses they support in order to make a larger impact. 
• You build genuine relationships with your customers: The industries we serve have relied on pen-and-paper solutions for years and haven’t been met with the type of technology we offer. Our customer's value earned trust and human relationships built over time.
• You want to be with the best: Samsara’s high-performance culture means you’ll be surrounded by the best and challenged to go farther than you have before. 
• You are a team player: At Samsara, sales is a team sport. We help each other out by sharing best practices and focusing on winning as a team.

In this role, you will: 

• Estimate revenue opportunity models & conduct impact analysis as part of the annual strategic planning cycle
• Leverage LLMs and other AI tools to systematically validate account level data - and provide dynamic quality measurement scores
• Generate models to drive data accuracy and triangulate from multiple inputs to establish sources of truth
• Establish data pipelines from internal data sources to generate baseline data programs
• Contribute to the development of predictive models and analytics, such as sales performance dashboards and pipeline forecasting tools, to improve operational efficiency.
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.

Minimum requirements for the role:

• M.S., or Bachelor's in Statistics, Computer Science, Math, Machine Learning, Economics or other related quantitative fields.
• 4+ years of relevant data science or advanced analytics work experience and 4+ years of experience building advanced analytical models/analysis to inform revenue strategy. 
• Advanced knowledge of revenue operations processes and systems. 
• Expertise in Tableau and/or similar platforms; expertise in coding languages and tools like Python, R and SQL - hands on coding experience is needed for this role. 
• Strong problem-solving, critical thinking, and communication abilities.
• Ability to manage multiple projects at the same time in a fast-paced business environment

An ideal candidate also has:

• Experience working with cutting edge LLM tools, web scraping, third-party data providers, or AI-driven automation.
• Experience analyzing B2B marketing strategies, channels and lifecycle identifying areas of opportunity and optimization.
• Familiarity with CRM tools and cloud-based data pipelines.
• ETL Experience: Proficiency in Extract, Transform, Load (ETL) tools and processes for building and managing data pipelines.
• Comfort working in an exploratory environment, testing and iterating on new data sources and methodologies.