Data Science Lead
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Summary
Leads and delivers multiple data-science engagements for oil & gas and petrochemical clients, owning the end-to-end project lifecycle from problem framing to delivery while acting as the main client contact. Core tech: AI/ML, NLP, GenAI, RAG/LLM, MLOps; cloud (AWS/Azure/GCP) and BI tools are pluses.
Role & responsibilities
Lead and execute multiple tier-1 client projects involving advanced Data Science techniques including AI, ML/NLP, GenAI, RAG & LLM.
Responsibility:
- Lead data science efforts (AI, ML, NLP, RAG, GenAI, optimization) for multiple client engagements in n Oil & Gas and downstream Petrochemical industries.
- Manage end-to-end Data Science project lifecycle from problem comprehension, analytical solution development to final delivery.
- Act as primary point of contact for client interactions, presentations, and requirement elicitation.
- Guide teams on advanced data science techniques ensuring alignment with business objectives.
- Collaborate cross-functionally with domain experts, business stakeholders, and technical teams to deliver robust analytical solutions.
Essential Skills (Must Have):
- Expert knowledge of data science algorithms, methodologies, and end-to-end lifecycle (AI/ML/GenAI), MLOps and post deployment service of AI models.
- Exceptional articulation skills to communicate technical concepts clearly to both technical and business stakeholders.
- Advanced analytical skills for strategic problem-solving and actionable insight generation.
Additional Skills (Good to Have):
- Exposure to cloud platforms (AWS/Azure/GCP), DevOps, deployment and scaling frameworks
- Experience in leading agile teams and agile methodology.
- Familiarity with data visualization tools (e.g., Tableau, Power BI).
- Understanding of data engineering principles and big data.