Responsibilities:
People management - Lead a team of software engineers, DS, DE, MLE, in the design, development, and delivery of software solutions.
Program management - Strong program leader that has run program management functions to efficiently deliver ML projects to production and manage its operations.
Work with Business stakeholders & customers in the Retail Business domain to execute the product vision using the power of AI/ML.
Scope out the business requirements by performing necessary data-driven statistical analysis.
Analyse and extract relevant information from large amounts of data and derive useful insights on a big-data scale.
Create labelling manuals and work with labellers to manage ground truth data and perform feature engineering as needed.
Work with software engineering teams, data engineers and ML operations team (Data Labellers, Auditors) to deliver production systems with your deep learning models.
Select the right model, train, validate, test, optimise neural net models and keep improving our image and text processing models.
Architecturally optimize the deep learning models for efficient inference, reduce latency, improve throughput, reduce memory footprint without sacrificing model accuracy.
Create and enhance model monitoring system that could measure data distribution shifts, alert when model performance degrades in production.
Streamline ML operations by envisioning human in the loop kind of workflows, collect necessary labels/audit information from these workflows/processes, that can feed into improved training and algorithm development process.
Maintain multiple versions of the model and ensure the controlled release of models.
Manage and mentor junior data scientists, providing guidance on best practices in data science methodologies and project execution.
**Skills : **
MS/PhD from reputed institution with a delivery focus.
5+ years of experience in data science, with a proven track record of delivering impactful data-driven solutions.
Delivered AI/ML products/features to production.
Seen the complete cycle from Scoping & analysis, Data Ops, Modelling, MLOps, Post deployment analysis.
Experts in Supervised and Semi-Supervised learning techniques. Hands-on in ML Frameworks - Pytorch or TensorFlow.
Hands-on in Deep learning models. Developed and fine-tuned Transformer based models. (Input output metric, Sampling technique)
Deep understanding of Transformers, GNN models and its related math & internals.
Exhibit high coding standards and create production quality code with maximum efficiency.
Hands-on in Data analysis & Data engineering skills involving Sqls, PySpark etc.