Senior Data Scientist

Job Description

As a Senior Data Scientist, you will be responsible for collecting, organizing, analyzing, and interpreting Truecaller data with a focus on NLP. In this role, you will be pivotal in advancing our work with large language models and on-device models across diverse regions. Your expertise will enhance our natural language processing, machine learning, and predictive analytics capabilities. What you bring in: 5+ years of experience in designing, developing, and deploying ML models at scale, with a focus on NLP-driven solutions. Strong background in Natural Language Processing (NLP), including text classification, entity recognition, language modeling, and transformer-based architectures. Experience in building and deploying models at scale, handling millions of messages efficiently while maintaining performance and accuracy. Also working with on-device models. Ability to not only build ML models but also take ownership of deploying them into production, ensuring scalability, reliability, and monitoring. Knowledge of anomaly detection, adversarial ML techniques, and risk modeling to identify and prevent spam and fraudulent messaging activities. Strong ability to take ML models from research and experimentation to production, working closely with ML engineers and data engineers. Expertise in machine learning libraries such as TensorFlow, PyTorch, pandas and Scikit-learn, along with NLP-specific tools like Hugging Face Transformers, spaCy with experience in TFlife, ONNX. Hands-on experience fine-tuning LLMs including transformer-based architectures (BERT, GPT, LLaMA, T5, etc.) for domain-specific applications, including knowledge distillation, quantization, and model compression for efficiency. Strong ability to design, refine, and optimize prompts for LLM-based applications, ensuring high-quality responses and reduced model hallucinations. Ability to leverage data driven decision by experimentation, and statistical analysis to improve models and business outcomes. Strong understanding of designing, testing, and optimizing prompts for LLM-based applications to improve model accuracy and efficiency. Programming knowledge in at least one language, such as Python or R. Preferably python. Expert knowledge of machine learning algorithms. Familiarity with database modelling and data warehousing principles with a working knowledge of SQL Experience in building and optimizing large-scale data processing systems using Spark/PySpark Strong ability to work cross-functionally with engineers, product managers, and business stakeholders to align ML solutions with company objectives. The impact you will create: Take a loosely defined business problem and break it into tractable data problems. For each data problem, clearly articulate the value of solving it, its impact, and its complexity. Collaborate with Product and Engineering to scope, design, and implement systems that solve complex business problems ensuring they are delivered on time and within scope. Design, develop, and optimize state-of-the-art NLP models for large-scale message classification, fraud detection, and spam filtering, impacting millions of users globally. Take full ownership of ML model development, deployment, and monitoring, ensuring models are production-ready, scalable, and cost-efficient. Lead data science projects from ideation to deployment, ensuring alignment with business objectives and timelines. Manage and analyze large datasets collected from multiple countries, ensuring data integrity and consistency. Stay updated on industry best practices and emerging technologies to drive innovation within the Data Team. You work collaboratively across systems and teams to solve user and business problems. You are expected to help define success and design and build the systems to achieve it. To work with the Product to decide on priorities and set direction, design solutions, and help the team implement them. It would be great if you also have: Understanding of Conversational AI Deploying NLP models in production Working knowledge of GCP components