Data Science Lead Job at Codvo AI

Job Description

We are looking for a Senior / Lead Data Scientist with strong expertise in Machine Learning, Deep Learning, and production-grade ML systems, particularly around time-series data, forecasting, and predictive modeling, along with hands-on experience in Generative AI (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI systems. This role requires someone who can design, build, optimize, and productionize ML and GenAI solutions end-to-end, work closely with data engineering teams, and take ownership of complex AI workflows. Prior experience leading small teams or mentoring junior data scientists is strongly preferred.

Job Responsibility

Design, develop, and deploy production-grade ML and DL models with a focus on time-series data, forecasting, and predictive analytics
Build and optimize end-to-end ML pipelines, from data preprocessing and feature engineering to model training, evaluation, deployment, and monitoring
Apply advanced ML techniques including regression, tree-based models, ensemble methods, deep learning, and optimization algorithms
Perform feature extraction and dimensionality reduction using techniques such as autoencoders for high-dimensional datasets
Track experiments, model performance, and metrics using industry-standard tools and best practices
Design and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG) systems for enterprise use cases such as analytics automation, knowledge assistants, and decision-support tools
Build document ingestion, chunking, embedding, and retrieval pipelines for structured and unstructured data using vector databases
Develop Agentic AI workflows that enable multi-step reasoning, tool usage, and autonomous task execution
Integrate LLMs with traditional ML systems to enhance explainability, insights generation, and user interaction
Implement guardrails and evaluation mechanisms to reduce hallucinations and ensure reliable, grounded LLM outputs
Optimize LLM inference for latency, cost, and scalability in cloud and hybrid environments

Requirements

7+ years of hands-on experience in Data Science, Machine Learning, or Applied AI roles
Strong foundation in statistical modeling and machine learning, including: Regression, boosting trees, random forests, Time-series modeling and forecasting, Optimization techniques (linear, nonlinear, stochastic)
Deep Learning expertise using frameworks such as: TensorFlow, Keras, PyTorch
Experience with RNN, LSTM, GRU, CNN is a plus
Experience with NLP and unstructured data processing
Hands-on experience with LLMs and GenAI, including: Retrieval-Augmented Generation (RAG), Vector databases (FAISS, Chroma, Pinecone, or similar), Prompt engineering and LLM evaluation, Agentic AI frameworks (e.g., LangChain, LangGraph, or similar)
Strong programming skills in Python (R is a plus)
familiarity with sh/bash scripting
Experience working with SQL and NoSQL databases
Experience building and consuming REST APIs and web services
Exposure to Big Data tools (Spark, Hadoop, or similar) is a strong plus
Cloud experience (AWS / GCP / Azure)
exposure to GenAI platforms (AWS Bedrock, Azure OpenAI, Vertex AI) is a plus

Nice to have

Experience with RNN, LSTM, GRU, CNN is a plus
Exposure to Big Data tools (Spark, Hadoop, or similar) is a strong plus
Exposure to GenAI platforms (AWS Bedrock, Azure OpenAI, Vertex AI) is a plus

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Data Science Lead

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