- Senior
- Office in Chennai
Lead Data Scientist
Location: Chennai
Experience: 10+ years
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
