Key Responsibilities

Project and Practice Leadership

<ul>
<li>Own large-scale AI/ML programs, ensuring technical excellence, client satisfaction, and measurable business impact.</li>
</ul>

<ul>
<li>Lead client relationships – drive scoping, solution design, delivery, and executive stakeholder alignment.</li>
</ul>

<ul>
<li>Grow and guide cross-functional teams, setting direction, coaching talent, and ensuring delivery quality.</li>
</ul>

<ul>
<li>Contribute to practice growth through thought leadership, go-to-market development, and capability building.</li>
</ul>

Generative AI Expertise

<ul>
<li>Define GenAI strategy and roadmaps for enterprise clients across NLP, CV, and multimodal use cases.</li>
</ul>

<ul>
<li>Oversee architecture of agentic AI systems, orchestrating multi-step workflows with LangChain, LlamaIndex, AutoGen, and enterprise integrations.</li>
</ul>

<ul>
<li>Establish governance and optimization frameworks for AI agents and RAG pipelines (Pinecone, Weaviate, FAISS, Milvus).</li>
</ul>

<ul>
<li>Advise C-level stakeholders on responsible AI adoption, innovation opportunities, and transformation outcomes.</li>
</ul>

Cloud Platform Expertise

<ul>
<li>Architect and scale cloud-native AI platforms on Azure, AWS, or GCP with focus on compliance, scalability, and cost efficiency.</li>
</ul>

<ul>
<li>Standardize MLOps frameworks for lifecycle management, monitoring, and governance using SageMaker, Azure ML, or Vertex AI.</li>
</ul>

<ul>
<li>Ensure seamless integration of AI solutions with enterprise data ecosystems (data lakes, warehouses, ERP/CRM, real-time pipelines).</li>
</ul>

<ul>
<li>Data Science and Engineering</li>
</ul>

<ul>
<li>Drive high-impact AI/ML solutions (predictive analytics, recommendation engines, optimization models) with measurable ROI.</li>
</ul>

<ul>
<li>Set standards for enterprise data and ML pipelines, enabling scalable and production-ready AI applications.</li>
</ul>

<ul>
<li>Guide innovation and adoption of advanced analytics and multimodal data architectures.</li>
</ul>

Business Impact and Innovation

<ul>
<li>Translate executive business priorities into AI-driven strategies with clear outcomes.</li>
</ul>

<ul>
<li>Shape transformation programs by identifying high-value GenAI/agentic AI opportunities.</li>
</ul>

<ul>
<li>Represent the firm as a thought leader in Data &amp; AI through client advisory, assets, and industry contributions.</li>
</ul>

Required Qualifications

<ul>
<li>Experience: 10+ years in data science, including team leadership and independent client delivery.</li>
</ul>

<ul>
<li>Education: Master’s Degree in a quantitative discipline (e.g., Computer Science, Statistics, Applied Mathematics, or related field).</li>
</ul>

<ul>
<li>Industry Knowledge: Experience in Consumer Tech, Enterprise Tech, or Semiconductors.</li>
</ul>

Technical Skills

<ul>
<li>Programming: Proficiency in Python, SQL, and libraries for NLP and Computer Vision (e.g., Hugging Face, OpenCV).</li>
</ul>

<ul>
<li>GenAI Expertise: Hands-on experience in model development (selection, fine-tuning), model evaluation and optimization, deployment architecture (design, API/integration design), multi-step reasoning workflows, and autonomous agents</li>
</ul>

<ul>
<li>Cloud Platforms: Experience with Azure and/or AWS in data science workflows.</li>
</ul>

<ul>
<li>Data Science Tools: Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).</li>
</ul>

<ul>
<li>GenAI &amp; LLM Tooling : LangChain, LlamaIndex, Hugging Face Transformers, vector databases (e.g., FAISS, Pinecone) </li>
</ul>

<ul>
<li>Familiarity with cloud platforms like AWS, Azure, or GCP, especially services like S3, Redshift, BigQuery, or Azure Data Lake.</li>
</ul>

Preferred Skills

<ul>
<li>Experience in decision science projects focused on marketing, supply chain, or sales.</li>
</ul>

<ul>
<li>Strong analytical and problem-solving skills with a results-oriented mindset.</li>
</ul>

<ul>
<li>Excellent communication and stakeholder management capabilities.</li>
</ul>

Data Science

Key Responsibilities Project and Practice Leadership Own large-scale AI/ML programs, ensuring technical excellence, client satisfaction, and measurable business impact. Lead client relationships – drive scoping, solution design, delivery, and executive stakeholder alignment. Grow and guide cross-functional teams, setting direction, coaching talent, and ensuring delivery quality. Contribute to practice growth through thought leadership, go-to-market development, and capability building. Generative AI Expertise Define GenAI strategy and roadmaps for enterprise clients across NLP, CV, and multimodal use cases. Oversee architecture of agentic AI systems, orchestrating multi-step workflows with LangChain, LlamaIndex, AutoGen, and enterprise integrations. Establish governance and optimization frameworks for AI agents and RAG pipelines (Pinecone, Weaviate, FAISS, Milvus). Advise C-level stakeholders on responsible AI adoption, innovation opportunities, and transformation outcomes. Cloud Platform Expertise Architect and scale cloud-native AI platforms on Azure, AWS, or GCP with focus on compliance, scalability, and cost efficiency. Standardize MLOps frameworks for lifecycle management, monitoring, and governance using SageMaker, Azure ML, or Vertex AI. Ensure seamless integration of AI solutions with enterprise data ecosystems (data lakes, warehouses, ERP/CRM, real-time pipelines). Data Science and Engineering Drive high-impact AI/ML solutions (predictive analytics, recommendation engines, optimization models) with measurable ROI. Set standards for enterprise data and ML pipelines, enabling scalable and production-ready AI applications. Guide innovation and adoption of advanced analytics and multimodal data architectures. Business Impact and Innovation Translate executive business priorities into AI-driven strategies with clear outcomes. Shape transformation programs by identifying high-value GenAI/agentic AI opportunities. Represent the firm as a thought leader in Data &amp; AI through client advisory, assets, and industry contributions. Required Qualifications Experience: 10+ years in data science, including team leadership and independent client delivery. Education: Master’s Degree in a quantitative discipline (e.g., Computer Science, Statistics, Applied Mathematics, or related field). Industry Knowledge: Experience in Consumer Tech, Enterprise Tech, or Semiconductors. Technical Skills Programming: Proficiency in Python, SQL, and libraries for NLP and Computer Vision (e.g., Hugging Face, OpenCV). GenAI Expertise: Hands-on experience in model development (selection, fine-tuning), model evaluation and optimization, deployment architecture (design, API/integration design), multi-step reasoning workflows, and autonomous agents Cloud Platforms: Experience with Azure and/or AWS in data science workflows. Data Science Tools: Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). GenAI &amp; LLM Tooling : LangChain, LlamaIndex, Hugging Face Transformers, vector databases (e.g., FAISS, Pinecone) Familiarity with cloud platforms like AWS, Azure, or GCP, especially services like S3, Redshift, BigQuery, or Azure Data Lake. Preferred Skills Experience in decision science projects focused on marketing, supply chain, or sales. Strong analytical and problem-solving skills with a results-oriented mindset. Excellent communication and stakeholder management capabilities.

Data Science in Mumbai (UID-15731)

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