About Clear Demand: Clear Demand is the leader in AI-driven price and promotion optimization for retailers. Our platform transforms pricing from a challenge to a competitive advantage, helping retailers make smarter, data-backed decisions across the entire pricing lifecycle. By integrating competitive intelligence, pricing rules, and demand modelling, we enable retailers to maximize profit, drive growth, and enhance customer loyalty — all while maintaining pricing compliance and brand integrity. With Clear Demand, retailers stay ahead of the market, automate complex pricing decisions, and unlock new opportunities for growth.
Key Responsibilities:
Vision & strategy – Set the AI/ML centric tech & product strategy for the organization. Lead & inspire the organization with it.
People management - Lead a team of software engineers, DS, DE, MLE, ML/DS managers in the design, development, and delivery of AI enabled 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.
Product Management - Scope out the business requirements by performing necessary data-driven statistical analysis.
Set goals and, objectives using proper business metrics and constraints.
AI/ML tech management - Conduct exploratory analysis on large volumes of data, understand the statistical shape, and use the right visuals to drive & present the 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 both deep learning & traditional ML models.
Select the right model, train, validate, test, regularize, optimise and keep improving multi-modal, text and traditional ML models.
Architecturally optimize the models for efficient inference, reduce latency, improve throughput, reduce memory footprint without sacrificing model accuracy.
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
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 data scientists, providing guidance on best practices in data science methodologies and project execution.
Lead cross-functional teams in the delivery of data-driven projects, ensuring alignment with business goals and timelines.
Collaborate with stakeholders to define project objectives, deliverables, and timelines.
Qualifications & Experience:
MS/PhD from reputed institution with a delivery focus.
10+ 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 Regression(log-log regression), 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.
Exposure to ML & Data services on the cloud – AWS, Azure, GCP Understanding internals of computer hardware - CPU, GPU, TPU is a plus.
Can leverage the power of hardware accelerators to optimize the model execution —PyTorch Glow, cuDNN, is a plus