- 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 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