Data Science Lead (India) (Remote)

<div><span><div><strong>Company Overview</strong></div><div>At <strong>Codvo</strong>, software and people transformations go hand-in-hand. We are a global empathy-led technology services company where product innovation and mature software engineering are part of our core DNA. <strong>Respect, Fairness, Growth, Agility, and Inclusiveness</strong> are the core values we aspire to live by every day.</div><div>We continue to expand our <strong>digital strategy, architecture, AI/ML, GenAI, and product engineering</strong> capabilities to deliver outside-the-box thinking and measurable business outcomes for our clients.</div><hr><div><strong>Role Overview</strong></div><div>We are looking for a <strong>Senior / Lead Data Scientist</strong> with strong expertise in <strong>Machine Learning, Deep Learning, and production-grade ML systems</strong>, particularly around <strong>time-series data, forecasting, and predictive modeling</strong>, along with hands-on experience in <strong>Generative AI (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI systems</strong>.</div><div>This role requires someone who can <strong>design, build, optimize, and productionize ML and GenAI solutions end-to-end</strong>, work closely with data engineering teams, and take ownership of complex AI workflows. Prior experience <strong>leading small teams or mentoring junior data scientists</strong> is strongly preferred.</div><hr><div><strong>Key Responsibilities</strong></div><div><strong>Core ML &amp; Predictive Analytics</strong></div><ul><li>Design, develop, and deploy <strong>production-grade ML and DL models</strong> with a focus on <strong>time-series data, forecasting, and predictive analytics</strong>.</li><li>Build and optimize <strong>end-to-end ML pipelines</strong>, from data preprocessing and feature engineering to model training, evaluation, deployment, and monitoring.</li><li>Apply advanced ML techniques including <strong>regression, tree-based models, ensemble methods, deep learning, and optimization algorithms</strong>.</li><li>Perform <strong>feature extraction and dimensionality reduction</strong> using techniques such as <strong>autoencoders</strong> for high-dimensional datasets.</li><li>Track experiments, model performance, and metrics using industry-standard tools and best practices.</li></ul><div><strong>GenAI, LLMs &amp; Agentic AI</strong></div><ul><li>Design and implement <strong>LLM-powered applications</strong>, including <strong>Retrieval-Augmented Generation (RAG)</strong> systems for enterprise use cases such as analytics automation, knowledge assistants, and decision-support tools.</li><li>Build <strong>document ingestion, chunking, embedding, and retrieval pipelines</strong> for structured and unstructured data using vector databases.</li><li>Develop <strong>Agentic AI workflows</strong> that enable multi-step reasoning, tool usage, and autonomous task execution.</li><li>Integrate LLMs with traditional ML systems to enhance <strong>explainability, insights generation, and user interaction</strong>.</li><li>Implement <strong>guardrails and evaluation mechanisms</strong> to reduce hallucinations and ensure reliable, grounded LLM outputs.</li><li>Optimize LLM inference for <strong>latency, cost, and scalability</strong> in cloud and hybrid environments.</li></ul><div><strong>Required Skills – Technical</strong></div><ul><li><strong>7+ years</strong> of hands-on experience in <strong>Data Science, Machine Learning, or Applied AI</strong> roles.</li><li>Strong foundation in <strong>statistical modeling and machine learning</strong>, including:<ul><li>Regression, boosting trees, random forests</li><li><strong>Time-series modeling and forecasting</strong></li><li>Optimization techniques (linear, nonlinear, stochastic)</li></ul></li><li><strong>Deep Learning expertise</strong>using frameworks such as:<ul><li>TensorFlow, Keras, PyTorch</li><li>Experience with <strong>RNN, LSTM, GRU, CNN</strong> is a plus</li></ul></li><li>Experience with <strong>NLP and unstructured data processing</strong>.</li><li>Hands-on experience with <strong>LLMs and GenAI</strong>, including:<ul><li><strong>Retrieval-Augmented Generation (RAG)</strong></li><li>Vector databases (FAISS, Chroma, Pinecone, or similar)</li><li>Prompt engineering and LLM evaluation</li><li>Agentic AI frameworks (e.g., LangChain, LangGraph, or similar)</li></ul></li><li>Strong programming skills in <strong>Python</strong> (R is a plus); familiarity with <strong>sh/bash scripting</strong>.</li><li>Experience working with <strong>SQL and NoSQL databases</strong>.</li><li>Experience building and consuming <strong>REST APIs and web services</strong>.</li><li>Exposure to <strong>Big Data tools</strong> (Spark, Hadoop, or similar) is a strong plus.</li><li><strong>Cloud experience</strong> (AWS / GCP / Azure); exposure to GenAI platforms (AWS Bedrock, Azure OpenAI, Vertex AI) is a plus.</li></ul></span></div>