Lead Data Scientist
1 week ago
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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