Lead Data Scientist

2 days ago

Noida, 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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