Lead Data Scientist

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.