At MicroSave Consulting, the Ethical AI and Data Solutions team works on applying data, analytics, and AI to complex institutional, development, and sectoral challenges. The Assistant Manager role is an independent technical delivery role that combines analytical ownership, problem solving, client-facing engagement, and support to junior team members. This role is suited to professionals who want to lead substantial analytical work, apply AI and data science in real-world contexts, and grow into stronger technical and delivery leadership over time.

Responsibilities:

• Independently deliver analytical workstreams across data analytics, machine learning, AI applications, and research-driven problems solving
  
• Participate in project setup discussions to understand client needs, define analytical requirements, and translate them into structured technical approaches
  
• Apply statistical, econometric, machine learning, and AI techniques to solve practical business, policy, operational, and research questions
  
• Lead end-to-end analysis including data extraction, cleaning, transformation, feature engineering, modeling, validation, interpretation, and presentation of findings
  
• Work with structured and unstructured data, including tabular, text, transactional, operational, survey, image, and time-series datasets where relevant
  
• Build, test, and refine analytical and machine learning workflows appropriate to the assignment context
  
• Develop clear and compelling visualizations, dashboards, presentations, and analytical notes for technical and non-technical audiences
  
• Contribute to report writing and presentation development, with a focus on translating technical outputs into useful and actionable insights
  
• Collaborate with Managers and other team members to assess technical feasibility, methodological choices, and implementation pathways
  
• Review the work of Associates and provide guidance on analytical quality, coding practices, and clarity of outputs
  
• Support the development of internal tools, templates, reusable code assets, and knowledge products within the Ethical AI and Data Solutions team
  
• Contribute to pilots, prototypes, and AI-enabled solutions involving areas such as NLP, LLMs, forecasting, automation, or other applied use cases where relevant
  

Additional responsibilities:

• Participate actively in assignment setup and debriefing meetings, especially on methodological choices, delivery risks, and technical lessons for future work
  
• Conduct Quality of Delivery reviews for analytical outputs and help ensure rigor, consistency, and reproducibility across assignments
  
• Mentor junior team members through structured feedback, review, and day-to-day problem solving
  
• Take ownership of self-development by keeping pace with advancements in machine learning, generative AI, LLMs, evaluation methods, and emerging analytical tools
  
• Contribute to internal capability-building through training materials, knowledge notes, demonstrations, and team learning sessions
  

Requirements

Qualifications

• Master’s degree preferred in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a related quantitative field from a reputed institution
  
• Bachelor’s degree may be considered for candidates with strong applied experience in analytics, machine learning, or AI delivery
  
• Familiarity with machine learning and AI frameworks such as scikit-learn, TensorFlow, PyTorch, Hugging Face, or similar tools is preferred
  
• Additional certifications in analytics, cloud platforms, machine learning, or applied AI will be an advantage
  

Key skills

• Strong proficiency in Python and SQL
  
• Experience with statistical analysis, machine learning, and applied data science methods
  
• Strong data visualization and analytical storytelling capability
  
• Ability to translate problem statements into structured analytical plans
  
• Strong written, verbal, and stakeholder communication skills
  

Any other requirement

• 3 to 6 years of experience applying statistical, econometric, machine learning, and AI techniques to real-world projects
  
• Experience handling a range of data formats such as tabular, text, survey, operational, image, or time-series data is preferred
  
• Experience with model evaluation, validation, error analysis, and interpretation is expected
  
• Familiarity with APIs, cloud environments, deployment workflows, or model operationalization is a strong asset
  
• Exposure to NLP, transformers, LLM-based applications, forecasting, recommendation systems, or computer vision will be an advantage
  
• Ability to work independently with limited supervision while maintaining quality and delivery timelines
  
• Prior experience reviewing junior colleagues’ work or mentoring smaller teams is preferred
  
• Strong ownership mindset and comfort working across ambiguity, evolving scopes, and mixed technical-business contexts are essential