Role Proficiency:

Design and develop ML solutions that will enable intelligent experiences and provide value. Collaboratively work with business technology and product teams to understand the product objectives and formulate the ML problem under minimal guidance from Lead II
 

Outcomes:

1.       Executes relevant data wrangling activities related to the problem
2.       Conduct ML experiments to understand feasibility; building baseline models to solve the business problem
3.       Fine tune the baseline model for optimum performance
4.       Test Models internally per acceptance criteria from the business
5.       Identify areas and techniques to optimize the model based on test results
6.       Document relevant artefacts for communicating with the business
7.       Work with data scientists to deploy the models.
8.       Work with product teams in planning and execution of new product releases.
9.       Set OKRs and success steps for self/ team and provide feedback of goals to team members
10.   Identify metrics for validating the models and communicate the same in business terms to the product teams.
11.   Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions
     

Measures of Outcomes:

1.       Selection of right algorithms for the business problems
2.       Successful deployment of the model with optimised accuracy for baseline model
3.       Number of time project schedule / timelines adhered to
4.       Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)
5.       Number of internal testing observations published and models refined to achieve 100 % business objectives with mentoring from the Lead ML Engineer
6.       Number of business metric and corresponding model metrics identified independently or with assistance from product team / ML Specialist
7.       Number of areas identified for improving the model using new technologies for product / feature improvements
8. Number of Rapid prototypes using state of the art methods

Outputs Expected:

Design to deliver Product Objectives:

1. Design ML solutions which are aligned to and achieve product objectives
    
2. Understand the business requirements; formulate into an ML problem
    
3. Define data requirements for the model building and model monitoring; working with product managers to get necessary data
    
4. Define the data requirement for the problem
    
5. Define the AI scope and metrics from the product and business objectives with guidance from Lead II
    
6. Identify technology components for Rapid prototype
    
7. Alignment of Business metrics to Model Metrics
    
8. Check the validity of the training data and test data requirements from the performance standpoint and take necessary actions

Updated on state of art techniques in the area of AI / ML :

1. Perform necessary research to use the latest state of the art techniques to design scalable approaches
    
2. Explain the relevance of the technologies its pros and cons to the product team to enable appropriate design experiences
    

Skill Examples:

1.      Technically strong with the ability to connect the dots
2.      Ability to communicate the relevance of technology to the stakeholders in a simple and relatable language
3.      Capability in selecting the appropriate techniques based on the data availability and set expectations on the overall functionality of the solutions
4.      Ability to understand the limitations of the current technology; defining the AI scope and metrics
5.      Curiosity to learn more about new business domains and Technology Innovation
6. An empathetic listener who can give and receive honest thoughtful feedback

Knowledge Examples:

•       Expertise in machine learning model building lifecycle
  1.       Clear understanding of various ML techniques and its appropriate use to business problems
  2.       A strong background of Statistics and Mathematics
  3.       Expertise in one of the domains – Computer Vision Language Understanding or structured data
  4.       Experience in executing collaboratively with engineering design user research teams and business stakeholders
  5.       Experience with data wrangling techniques preprocessing and post processing requirements for ML solutions
  6.       Aware of the techniques of validating the quality of the data
  7.       Experience in identifying the testing criteria to validate the quality of the model output
  8.       Good knowledge python and deep learning frameworks like Tensorflow Pytorch Caffe
  9.       Familiar with the machine learning model testing approaches
  10.       A genuine eagerness to work and learn from a diverse and talented team