Example questions or scenarios:

• "Implement an algorithm to find the longest substring without repeating characters and optimize it for O(N) time complexity."
• "Write a Python function to merge overlapping intervals in a dataset of customer activity timestamps."

Machine Learning System Design

At Freshworks, data scientists are expected to build systems that scale. The system design round evaluates your ability to conceptualize end-to-end architectures, considering data ingestion, model training, deployment, and real-time inference.

You will often be asked to design a specific system relevant to the company's domain, such as a conversational agent or an automated ticketing workflow.

Be ready to go over:

• Chatbot System Design – Architecting conversational interfaces, intent recognition, slot filling, and dialogue management.
• Scalability & Latency – Designing systems that can handle thousands of queries per second with sub-second response times.
• Model Monitoring – Setting up pipelines to detect data drift, model decay, and latency spikes in production.

Example questions or scenarios:

• "Design a chatbot system for a platform like Paytm Insider or BookMyShow. How would you handle slot filling for seat selection and movie preferences?"
• "How would you architect an automated email response system that drafts replies for support agents based on historical ticket data?"

As a Data Scientist at Freshworks, your day-to-day activities will span the entire machine learning lifecycle, from initial research to production deployment. You will be embedded in product teams, collaborating closely with software engineers, product managers, and UX designers to build intelligent features.

Your primary responsibilities will include:

• Designing, training, and deploying machine learning models and NLP pipelines to power conversational AI and automation features across Freshworks products.
• Collaborating with product managers to translate business requirements into technical data science formulations.
• Working alongside engineering teams to integrate models into production systems, ensuring high availability, scalability, and low latency.
• Conducting exploratory data analysis to identify patterns, generate hypotheses, and extract actionable insights from large-scale customer interaction datasets.
• Fine-tuning and evaluating Large Language Models (LLMs) to customize their behavior for specific enterprise use cases.
• Establishing robust validation frameworks, monitoring systems, and feedback loops to continuously improve model performance and reliability in production.

To be competitive for the Data Scientist position at Freshworks, you must demonstrate a strong technical foundation coupled with practical experience in building and deploying machine learning systems.

Must-Have Skills

• Technical Expertise – Strong proficiency in Python and SQL, along with deep familiarity with machine learning libraries such as PyTorch, TensorFlow, Scikit-Learn, and Hugging Face.
• Core ML/NLP Knowledge – Solid understanding of supervised and unsupervised learning, natural language processing, and deep learning architectures.
• Problem-Solving – Excellent algorithmic problem-solving skills, with the ability to write clean, optimized code.
• Experience – A proven track record of building and deploying machine learning models in a production environment.

Nice-to-Have Skills

• SaaS Domain Experience – Prior experience working in a SaaS, CRM, or customer support technology domain.
• Generative AI & LLMs – Hands-on experience with prompt engineering, fine-tuning LLMs, and building RAG systems.
• Cloud & MLOps – Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps tools for model tracking and deployment (e.g., MLflow, Kubeflow).

Q: How technical is the interview process for Data Scientists at Freshworks? A: The process is highly technical and rigorous. You will be evaluated on your mathematical understanding of algorithms, your ability to write optimized code, and your capacity to design scalable machine learning systems. Expect to write code in almost every technical round.

Q: What is the typical timeline for the interview process? A: The process is generally quick, often concluding within two to three weeks from the initial HR screen to the final decision. However, this can vary depending on the team's urgency and whether the process includes a take-home assignment.

Q: How important is NLP and LLM experience for this role? A: It is highly important. Because Freshworks is heavily invested in conversational AI and generative AI capabilities (such as Freddy AI), candidates with strong backgrounds in NLP, LLMs, and chatbot development are highly sought after.

Q: Are the coding rounds conducted in an IDE or a plain text editor? A: Candidates have reported being asked to write code on plain text editors like Notepad or whiteboards during in-person interviews. It is critical to practice writing clean, syntactically correct code without relying on IDE autocomplete features.

• Clarify Requirements Early: In system design and coding rounds, do not jump straight into writing code or drawing architectures. Ask clarifying questions to understand the constraints, scale, and specific use cases of the problem.

• Optimize Your Solutions: When solving coding questions, always start with a functional solution, but immediately follow up with how you can optimize it. Interviewers at Freshworks value candidates who proactively discuss time and space complexity.

• Brush Up on the SaaS Context: Understand the core business of Freshworks. Think about how machine learning can improve customer support efficiency, reduce response times, and help sales teams close deals faster. Aligning your answers with these business goals will make a strong impression.

• Master the Fundamentals: Do not skip the basics. Be prepared to explain standard concepts like PCA, categorical encoding, and basic statistics just as thoroughly as advanced transformer models.

Becoming a Data Scientist at Freshworks offers an exciting opportunity to work on cutting-edge AI technologies at a massive scale. By powering features like Freddy AI, your models will directly improve the daily workflows of millions of users worldwide. The interview process is designed to find candidates who are not only technically brilliant but also product-minded and collaborative.

To maximize your chances of success, focus your preparation on core machine learning theory, robust coding practices, and end-to-end system design—especially around NLP and conversational systems. Practicing live coding without an IDE and refining your ability to explain complex mathematical concepts simply will set you apart from other candidates.

For more detailed interview experiences, real-world questions, and preparation resources tailored to top tech companies, explore the comprehensive guides available on Dataford.

The compensation for a Data Scientist at Freshworks is highly competitive and scales with your experience and expertise. The package typically includes a strong base salary, performance-based bonuses, and equity options (RSUs). When reviewing compensation data, consider the complete value of the offer, including the growth potential of the equity component as Freshworks continues to expand its global footprint.