Lead Data Scientist

Job Description As a Lead Data Scientist at Datakulture, you will drive end-to-end delivery of data science projects, transforming business problems into analytical solutions. You will 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: - Have a Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. - Have 7+ 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 As a Lead Data Scientist at Datakulture, you will drive end-to-end delivery of data science projects, transforming business problems into analytical solutions. You will 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: - Have a Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. - Have 7+ 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