Lead Data Scientist

4 days ago

New Delhi, India KiE Square Analytics Full time

KiE Square is looking for a passionate and experienced Full Stack Data Scientist to join our Analytics Consulting team. The ideal candidate shall possess a strong blend of technical expertise, analytical thinking, and business consulting skills. You will be responsible for developing and deploying scalable machine learning solutions, building end-to-end data pipelines, and translating analytical insights into actionable business strategies for our clients.Responsibilities Partner with clients to understand business challenges and identify data-driven opportunities. Design, build, and deploy end-to-end machine learning models and analytical solutions tailored to client objectives. Develop data ingestion, transformation, and modelling pipelines using modern data engineering tools. Translate analytical outcomes into clear business insights, dashboards, and recommendations. Work closely with consulting teams to integrate analytics into decision-making frameworks. Build and maintain production-ready APIs and ML workflows for scalable model deployment. Implement MLOps practices for continuous integration, deployment, and performance monitoring. Mentor junior team members and contribute to internal best practices in analytics and data science.Required Skills & Qualifications Education: B.Tech/M.Tech in Computer Science (with Data Science Masters is a plus) Experience: 5–7 years in analytics consulting, applied data science, or end-to-end AI/ML solution development. Proven experience in problem framing, model development, and production deployment. Proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.) and SQL. Hands-on experience on at least two of the top platforms such as Google Vertex AI, Amazon Sagemaker, Azure Databaricks. Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents). Hands-on experience with data engineering tools (Airflow, Spark, or Databricks) .Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization. Exposure to cloud environments such as AWS, Azure, or GCP. Ability to analyse structured and unstructured data from multiple sources. Experience in model interpretability, A/B testing, and business impact measurement. Strong client communication, storytelling, and presentation skills. Good to Have Exposure to data visualization tools such as Power BI, Tableau, AWS Quicksight or Looker. Understanding of consulting frameworks and client delivery models. Familiarity with API development (Flask, FastAPI) and containerization (Docker, Kubernetes). Experience with LLMs or Generative AI use cases is a plus.

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