Responsibilities:

• Lead client-facing engagements across data, analytics, AI, and research-driven problem solving
  
• Work with clients and internal teams to understand business, policy, operational, and research challenges, and translate them into structured analytical and advisory approaches
  
• Shape project scope, define workplans, identify key questions, and guide teams toward practical, high-quality solutions
  
• Lead end-to-end delivery of assignments, including problem framing, analysis, synthesis, development of insights, and preparation of client-ready outputs
  
• Apply and guide the use of data analysis, statistical methods, machine learning, and AI techniques where relevant to strengthen decision-making and problem solving
  
• Translate complex technical and analytical findings into clear recommendations, strategic implications, and actionable next steps for clients
  
• Lead the development of reports, presentations, dashboards, briefs, and decision-support materials for technical and non-technical stakeholders
  
• Facilitate client meetings, workshops, and problem-solving discussions to refine requirements, align stakeholders, and support decision-making
  
• Review analytical outputs, assumptions, visualizations, and written deliverables to ensure quality, clarity, and relevance
  
• Guide teams on analytical design, methodological choices, trade-offs, and implementation considerations
  
• Manage client relationships on ongoing assignments, including communication on progress, risks, deliverables, and expectations
  
• Mentor Associates and Assistant Managers through review, feedback, and day-to-day delivery guidance
  
• Support proposal development, opportunity scoping, technical write-ups, staffing inputs, and broader business development efforts
  
• Contribute to internal capability building, reusable assets, knowledge products, and practice development within the Ethical AI and Data Solutions team
  

Additional responsibilities:

• Lead assignment setup and debriefing meetings, with a focus on delivery quality, learning, and continuous improvement
  
• Conduct Quality of Delivery reviews to strengthen the quality and usability of outputs across projects
  
• Support internal learning through coaching, review frameworks, training inputs, and mentoring
  
• Stay updated on developments in analytics, AI, and related tools, and assess their relevance for client work and practice growth
  
• Encourage the responsible and effective use of data and AI in project delivery​
  

Requirements

Qualifications

• Master’s degree preferred in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, Management, Public Policy, or a related field from a reputed institution
  
• Bachelor’s degree may be considered for candidates with strong experience in analytics, consulting, applied research, or AI-enabled delivery
  
• Strong grounding in analytics, quantitative problem solving, and applied use of data is essential
  
• Familiarity with data science, machine learning, or AI tools and workflows is expected
  
• Additional certifications in analytics, AI, consulting, business strategy, or related domains will be an advantage
  

Key skills

• Consulting-led problem solving and structured thinking
  
• Strong grounding in analytics, machine learning, and AI applications
  
• Client and stakeholder management
  
• Proposal writing, business development support, and solution scoping
  
• Team leadership, review, and quality assurance
  

Any other requirement

• 6 to 10 years of relevant experience in analytics, data science, AI, consulting, applied research, or related roles
  
• Demonstrated experience leading client-facing assignments or workstreams end-to-end
  
• Ability to translate ambiguous business or policy questions into structured approaches and actionable recommendations
  
• Strong review capability across analytical outputs, written deliverables, and team workstreams
  
• Experience in proposal development, business development support, or practice-building is preferred
  
• Exposure to machine learning, AI applications, LLMs, or related tools is an advantage
  
• Strong ownership, judgment, and collaborative working style are essential