Role Proficiency:

Design develop and deploy ML models that provides accurate results with controls to solve the business problem identified using state of art techniques. Working under minimal guidance from Specialist II ML Engineering. Collaborate with cross functional teams - business technology and product teams to understand the product vision to ensure end to end deployment.

Outcomes:

      Identify and formulate business problem to AI / ML related problems. Identify and communicate AI Scope with stake holders       Execute relevant data wrangling activities related to the problem       Conduct ML experiments to understand the feasibility; building baseline models to solve the business problem       Fine tune the models for optimum performance       Test Models internally per acceptance criteria from the business       Identify areas and techniques to optimize the model based on test results       Work with product teams in planning and execution of new product releases.       Set OKRs and success steps for self/ team and provide feedback of goals to team members       Identify metrics for validating the models and communicate the same in business terms to the product teams.   Keep track of the trends and do rapid prototyping to understand the feasibility of using in existing solutions   Visualise and build more complex models / solutions which address scalable solutions   Work with product teams in planning and execution of new product releases   Mentor junior data scientists to help delivery of their solutions   Drive multiple ML Solution Design Development and End to End Deployment   Conduct Code Reviews and Solution Reviews of team members and provide constructive feedback to make solutions more robust   Work with multiple product design and product management teams; identifying design interventions of ML Models

Measures of Outcomes:

      Selection of appropriate algorithms for the business problems       Successful deployment of the model with optimised accuracy for baseline model       100 % Adherence to project schedule / timelines       Personal and team achievement of 100% of quarterly/yearly objectives (OKR Assignments HIG stretch goals) Publish internal testing observations and refine the model to achieve 100% of business objectives       Independently or with help of product team / ML Specialist identify business metrics and the corresponding model metrics.       Number of areas identified for improving the model using new technologies so product / feature improves.       Scalability of the ML solutions for complex problems       No gaps in requirements gathering and converting it to AI scope.       Work with cross functional team of stakeholders to deploy the model   Number of reusable components which enable faster deployment of solutions.   Number of end- to-end successful deployments

Outputs Expected:

Design to deliver Product Objectives:

Design ML solutions which are aligned
and achieve product objectives Understand the business requirements and formulate into an ML problem Define data requirements for the model building and model monitoring; working with product managers to get necessary data Define the data requirements for the problem Define the AI scope and metrics from the product and business objectives
with guidance from Lead II Check the validity of the training data and test data requirements from a performance standpoint and take necessary actions

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

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

Technology Innovation :

Convey innovation in the way of problem solving
publish the work in leader boards or as patents Identify reusable components

Skill Examples:

      Technically strong with the ability to connect the dots       Ability to communicate the relevance of technology to the stakeholders in a simple and relatable language       Ability to select appropriate techniques based on the data availability and set expectations on the overall functionality of the solutions       Understanding of the limitation of the current technology define the AI scope and metrics       Curiosity to learn more about new business domains and Technology Innovation       An empathetic listener who can give and receive honest thoughtful feedback       Ability to abstract problems across multiple projects and design reusable assets

Knowledge Examples:

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

Additional Comments:

7+ years of experience in machine learning, with a focus on forecasting, optimization, and causal inference. 3+ years of experience in big data processing and cloud-native infrastructure. Proficiency in Python, SQL, and ML libraries such as scikit-learn, Prophet, and optimization tools. Experience with Azure, Databricks, Docker, Kubernetes, and FastAPI. Strong understanding of retail domain including assortment planning, inventory optimization, and promotional analytics. Proven track record of deploying real-time ML systems and delivering measurable business impact (e.g., 3–5% sales lift).