Machine Learning Analyst / AMLS

• The ML Analyst position is open to all with prior training in Engineering or any related quantitative sciences discipline.
  
• No prior experience in Machine Learning or Deep Learning is required.
  
• Candidates with exposure to ML/DL (if any) are expected to have a clear and rigorous understanding of the concepts they are familiar with.
  
• Strong skills in data handling and logical problem-solving.
  
• Demonstrates quick learning ability and a strong work ethic.
  
• Willingness to take on any task, learn new tools, and adapt to evolving project needs.
  

• Work closely with data to support the development of ML and DL solutions.
  
• Conduct experiments under guidance and report results reliably.
  
• Learn to derive insights from experimental outcomes and determine appropriate next steps.
  
• Prepare, curate, and analyze datasets for training and evaluation.
  
• Monitor incoming data streams and perform regular quality checks.
  
• Assist in training and inference of ML models, including deep learning architectures.
  
• Contribute to well-documented and maintainable codebases.
  
• Document work clearly and consistently with high standards.
  
• Communicate and present experimental findings and results clearly within the team.
  
• Learn and apply best practices across ML development, coding, documentation, and experimentation.
  
• Collaborate effectively with project teams to meet milestones and deliverables.
  
• Proactively seek help and feedback when needed.
  
• Work efficiently with tools like Unix, VS Code, GitHub, and Docker.
  
• Develop proficiency with common ML tools and libraries such as Pandas, Scikit-learn, PyTorch, and Matplotlib.
  

Requirements

• ML Analysts will have a strong academic background in a quantitative field such as B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field, with 0–1 years of work experience.
  
• Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.
  
• Solid coding skills in one or more languages, including Python, C++, or Java.
  
• Interest in applying software engineering practices to ML projects.
  
• Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  
• Strong verbal and written communication skills in English.
  

Benefits

• Insurance Benefits - Medical Insurance, OPD Coverage, Accidental Insurance, Life Insurance.
  
• Wellness Program - Employee Assistance Program, Annual Health Check-Ups, Onsite Medical Centers, Emergency Support System.
  
• Parental Support - Maternity Benefit, Travel Reimbursement for Expectant Mothers during Last Trimester, Paternity Benefit, Day Care Support Program
  
• Mobility Benefits - Travel Policy, Relocation and Deputation benefits,
  
• Statutory Benefits - Employee PF Contribution, Gratuity, Leave Encashment
  
• Other Benefits – Unlimited Leave Policy, Professional Development Policy, Referral Bonuses, Internet and Telephone Allowance.