Lead Data Scientist

Role Overview

We are seeking a Lead Data Scientist who will act as the technical and strategic bridge between business, clients, and the delivery teams. The role demands full ownership of client-facing AI initiatives, from presales and proposal creation to architecture design, model development, and production deployment.

You will lead a cross-functional team of Data Scientists, MLOps Engineers, Data Engineers, and Backend Developers to deliver cutting-edge AI systems across Computer Vision, Audio AI, Agentic Systems, and RAG-based applications. This is both a client-facing and hands-on leadership role for someone passionate about solving real-world problems using data-driven intelligence and scalable AI architectures.

Key Responsibilities

1. Client Engagement & Pre-Sales Leadership

● Lead discovery and technical consultation calls with global prospects and clients.

● Translate complex AI opportunities into actionable proposals, SOWs, and RFP responses.

● Present architecture blueprints and proof-of-concept (POC) strategies to executive stakeholders.

● Work with business analysts and account managers to align AI solutions with ROI-driven goals.

● Define technical responses for RFPs and design winning proposals across Computer Vision, NLP, Audio AI, and Agentic automation domains.

1. Solution Architecture & Design

● Architect end-to-end AI systems — from data ingestion and feature engineering to model deployment.

● Design LLM-based RAG architectures, agentic multi-model systems, and GenAI tool integrations using LangChain, LangGraph, and vector databases.

● Create reference architectures for MLOps pipelines, ensuring versioning, observability, and compliance.

● Collaborate with cloud and backend teams for infrastructure scaling and CI/CD automation.

1. Technical Delivery & Team Leadership

● Lead and mentor a team of Data Scientists, AI/ML Engineers, MLOps, and Data Engineers through the complete AI lifecycle.

● Drive model research, training, fine-tuning, evaluation, and production-grade deployment.

● Ensure quality through rigorous code reviews, experimentation tracking (MLflow/DVC), and model monitoring frameworks.

● Balance hands-on development with strategic oversight — guiding both technical excellence and delivery velocity.

● Define and enforce data governance, process adherence, and documentation discipline across AI projects.

1. Research, Innovation & Continuous Improvement

● Champion experimentation in Generative AI, Computer Vision (SAM, UNet, Diffusion models), Audio Signal Processing, and LLM fine-tuning.

● Evaluate new frameworks and cloud AI offerings (e.g., Azure ML, AWS Sagemaker, Vertex AI).

● Contribute to internal AI accelerators.

● Mentor junior scientists, conduct internal AI workshops, and lead AI CoE knowledge sessions.

● Publish whitepapers, blogs, or internal case studies to reinforce the organization's AI thought leadership.

1. Governance & Stakeholder Communication

● Own AI project KPIs – accuracy, latency, scalability, and cost optimization.

● Establish cross-team communication standards between AI, backend, and DevOps groups.

● Provide executive-level reports summarizing technical progress, risk mitigation, and future roadmap.

● Ensure client satisfaction through structured communication, milestone alignment, and proactive updates.

Qualifications & Experience

● 6–10 years of experience in Data Science, ML Engineering, or Applied AI.

● Proven experience in leading end-to-end AI solution delivery and cross-functional teams.

● Strong academic or technical foundation in Statistics, ML, Deep Learning, and Data Engineering.

● Demonstrated portfolio in Computer Vision (UNet, SAM, Diffusion models), Audio AI (speech recognition, emotion classification), and LLM applications (LangChain, RAG, Agents).

● Solid expertise with Python, PyTorch, TensorFlow, FastAPI, LangChain, Hugging Face, OpenAI API, and Vector DBs (Pinecone, Weaviate, FAISS).

● Experience in cloud ecosystems (Azure, AWS, GCP) and CI/CD for ML (MLflow, DVC, Airflow, Docker, Kubernetes).

● Prior experience handling client communication, pre-sales proposals, and team leadership in a consulting or product environment.

Core Competencies

• Competency
• Description
• Strategic Thinking
• Ability to align AI initiatives with client business goals and organizational growth.
• Technical Leadership
• Guides multi-disciplinary teams through complex problem-solving with minimal oversight.
• Communication
• Delivers clear, structured updates to clients and executives; simplifies AI concepts for business users.
• Drives accountability for technical and business outcomes across all AI engagements.
• Learning & Innovation
• Continuously explores new frameworks, tools, and AI paradigms to strengthen internal capability.

Nice-to-Have

● Experience in A2A (Agent-to-Agent) orchestration and Multi-Agent collaboration systems.

● Knowledge of data compliance frameworks (GDPR, DPDP, HIPAA).

● Familiarity with business process automation using AI agents and workflow orchestration.

● Exposure to academic research or publications in applied ML domains.

Performance Indicators

● Successful closure of AI opportunities (conversion of proposals to projects).

● On-time, on-budget delivery of AI solutions with measurable impact.

● AI model performance metrics (accuracy, precision, latency) meeting or exceeding benchmarks.

● Team engagement and knowledge maturity across AI/ML pipelines.

● Client satisfaction and repeat business generation from technical trust-building.

Compensation & Growth Path

● Competitive compensation aligned with experience and impact.

● Growth to AI Practice Lead based on performance and portfolio expansion.

● Opportunity to represent the organization in conferences, client panels, and global AI forums.