Talent Acquisition Lead

Role Overview

We are looking for a strategic, research-driven Talent Acquisition Lead to drive targeted end-to-end recruitment for niche Data, AI, and Data Science roles. This role requires deep technical recruitment expertise, proactive headhunting, and talent mapping to engage passive candidates in non-volume hiring scenarios.

Key Responsibilities

End-to-End Talent Acquisition

• Full Lifecycle Hiring: Manage end-to-end recruitment across Data Engineering, Data Science, Machine Learning, Generative AI, MLOps, Data Architecture, Cloud Data, and Advanced Analytics.
  

• Sourcing Strategy: Translate complex technical job requirements into targeted sourcing strategies and build robust talent pipelines for immediate and future needs.
  

Headhunting & Targeted Search

• Proactive Search: Personally lead targeted headhunting efforts and map talent pools across companies, geographies, and technology stacks to engage high-caliber passive candidates.
  

• Relationship Building: Employ creative engagement approaches and foster long-term relationships with niche technical professionals.
  

Advanced Sourcing & Intelligence

• Digital Sourcing: Utilize LinkedIn Recruiter, GitHub, Boolean/X-ray search, professional communities, and digital channels to identify technical talent.
  

• Market Intelligence: Provide leadership with actionable insights on market trends, skills availability, compensation standards, competitor hiring patterns, and talent concentrations.
  

Stakeholder & Candidate Management

• Strategic Partnership: Partner closely with CTOs, CIOs, and engineering leaders as a trusted advisor to calibrate requirements and set hiring strategies.
  

• Offer Management: Articulate complex technology opportunities to candidates and effectively manage end-to-end offer discussions and negotiations.
  

Technical Focus Areas

Candidates must possess functional familiarity across the following technical domains to conduct intelligent screenings:

• Data & Engineering: Big Data, Data Warehousing, Data Architecture, Databricks, Snowflake, ETL/ELT, SQL.
  

• AI & Machine Learning: Machine Learning, Deep Learning, Generative AI/LLMs, NLP, Computer Vision, MLOps.
  

• Cloud & Modern Stack: AWS/Azure/GCP, Data Lakes/Lakehouse, Data Governance.
  

• Data Science & Analytics: Applied/ML/Decision Scientists, Product Analytics, Advanced Analytics, Business Intelligence.
  

Key Requirements & Differentiators

• Experience: 6–8 years of experience in technical recruitment with a proven track record of hiring niche Data, AI, and Data Science talent.
  

• Search Expertise: Demonstrated expertise in headhunting, passive candidate outreach, LinkedIn Recruiter, Boolean search, and structured market mapping.
  

• Technical Depth: Ability to differentiate subtle skill sets (e.g., Data Engineer vs. ML Engineer vs. GenAI Specialist) beyond simple keyword matching.
  

• Network: Strong network within top technology firms, AI companies, product organizations, or specialized consulting practices.
  

• Soft Skills: Exceptional communication, influencing, executive presence, and stakeholder management capabilities.
  

• Education: MBA or Postgraduate degree in HR or Business Management.