Lead Data Scientist

Lead Data Scientist Location: Chennai 
Experience: 10+ years  As a Lead Data Scientist at Datakulture, you will…  

• Drive end-to-end delivery of data science projects, transforming business problems into analytical solutions.  
• Lead a team of data scientists and ML engineers, providing technical guidance, code reviews, and mentorship.  
• Engage with clients and internal stakeholders during pre-sales to shape solution architecture, project scope, and value propositions.  
• Collaborate cross-functionally with domain experts, product managers, and engineers to ensure solutions are practical and impactful.  
• Stay updated on advancements in AI/ML and apply cutting-edge techniques to real-world problems.  
• Represent Datakulture in thought leadership initiatives—whitepapers, blogs, webinars, or conference talks. 

 Qualifications We'd love to hear from you if you... 

• Have a Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field.  
• Have 10+ years of hands-on experience in applied data science, statistical modeling, and machine learning.  
• Have a track record of delivering ML projects in production, across domains such as retail, finance, or operations.  
• Are skilled in Python, with strong knowledge of data manipulation, model development, and visualization.  
• Are proficient in Streamlit for building interactive data apps and demos for business stakeholders. 
• Have solid experience building and deploying APIs using FastAPI (or Flask). 
• Understand machine learning deployment workflows and ML Ops practices (e.g., model versioning, monitoring, CI/CD).  
• Have prior experience working with pre-sales or client-facing solutioning for analytics/AI projects.  
• Demonstrate strong problem-solving, communication, and team leadership skills. 

 Nice to have 

• Experience publishing research papers or contributing to open-source AI/ML projects.  
• Familiarity with modern NLP techniques, LLMs (fine-tuning, RAG, agents), and frameworks like LangChain or OpenAI APIs.  
• Working knowledge of cloud platforms (AWS, GCP, or Azure) and scalable ML infrastructure.  
• Exposure to BI tools (e.g., Power BI, Tableau) and data warehousing systems. 

 Our Technology Stack 

• Python and Jupyter Notebooks, SQL, Spark/PySpark 
• Tensorflow, PyTorch 
• Streamlit, Gradio, Flask 
• MLflow, Weights & Biases, Docker, Airflow, FastAPI, GitHub Actions 
• Github, Jira 
• AWS, Azure, GCP, Dataiku, Databricks