Role Description We are looking for a hands-on Senior Data Scientist with strong expertise in traditional Machine Learning, retail analytics, pricing optimization, and demand forecasting. The ideal candidate will have experience working in Retail, CPG, or E-Commerce environments and will play a key role in designing and scaling ML-driven pricing and promotion solutions. - Lead the design, development, and deployment of scalable machine learning solutions focused on pricing optimization, demand forecasting, and promotion planning. - Build and improve statistical and machine learning models for: - Demand forecasting - Price elasticity modeling - Promotion optimization - Inventory and revenue forecasting - Design, build and deploy robust ML pipelines in Databricks, build model monitoring systems, and production-ready APIs. - Ability to design and evaluate transformer-based time series forecasting models for large-scale retail sales forecasting and demand planning. - Drive experimentation, model evaluation, and continuous improvement of forecasting and pricing models. - Analyze large-scale structured and unstructured retail datasets to uncover trends, customer behavior patterns, and pricing insights. - Develop data-driven strategies that help maximize revenue, profitability, and pricing efficiency across products and categories. - Apply advanced statistical techniques and machine learning algorithms to solve complex retail business problems. - Collaborate closely with Product, Engineering, Data Engineering, and Business teams to translate business requirements into scalable ML solutions. - Mentor junior data scientists and provide technical guidance across cross-functional teams. - Communicate analytical findings and business recommendations clearly to both technical and non-technical stakeholders. Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. - 5+ years of hands-on experience in Machine Learning and Data Science. - Strong background in mathematics, probability, statistics, and regression analysis. - Solid understanding of traditional machine learning algorithms and statistical modeling techniques. - Strong programming expertise in Python, including: - Pandas - NumPy - Object-Oriented Programming (OOP) - Scikit-learn - TensorFlow or similar ML frameworks - Experience working with PySpark and large-scale data processing systems. - Strong SQL skills and experience working with relational databases and data warehouses. - Experience developing APIs and ML services using Flask or similar frameworks. - 5+ years of experience in Retail, CPG, or E-Commerce domains with expertise in: - Demand forecasting - Price elasticity - Promotion optimization - Pricing strategy - Experience with MLOps platforms and deployment pipelines such as: - Databricks - Large Language Models (LLMs) - Google Vertex AI - Amazon SageMaker - Azure Machine Learning - Experience building and maintaining CI/CD pipelines for ML workflows. Company Description Hypersonix.ai is disrupting the e-commerce space with AI, ML and advanced decision capabilities to drive real-time business insights. Hypersonix.ai has been built ground up with new age technology to simplify the consumption of data for our customers in various industry verticals.

• Design and deploy lightweight agents for editorial intelligence and revenue/ad operations • Pull and analyze data from sources like Google Ad Manager, Google Search Console, GA4, and third-party APIs • Surface anomalies, trends, and performance signals automatically • Deliver alerts, reports, and recommendations into Slack or Google Sheets • Use LLMs (Gemini or equivalent) to generate content briefs, scoring outputs, and editorial angles

• Support the development of data-driven features for pricing and promotion strategies to improve revenue and profitability. • Build, test, and maintain analytical models for demand forecasting, price sensitivity, and promotion performance with guidance from senior team members. • Apply data science, data analysis, machine learning, and statistical techniques to help solve retail business problems such as forecasting accuracy and promotion effectiveness. • Analyze structured and unstructured retail data to identify trends, understand customer behavior, and generate actionable insights. • Collaborate with cross-functional teams to implement and scale data solutions, and communicate findings through clear analysis and visualizations.

• Develop and implement machine learning models (classification, regression and segmentation) using a variety of Python libraries; • Validate machine learning models using diverse metrics and concepts such as confusion matrix, backtesting, out-of-time testing, train/test splits, cohorts, ROC-AUC and KS; • Handle data from different sources, consolidating it into usable structures for analysis. Data may not always be in the same environment or in the ideal format for model application; • Produce activity status updates as well as clear data process documentation to support solutions at the strategic level; • Create storytelling and prepare presentations to communicate modeling and analysis results; • Contribute to process changes by improving them through data to deliver measurable financial impact to the company; • Collaborate with Data Infrastructure, Data Engineering and Systems, and Process Engineering teams to integrate analytical solutions into existing internal tools and processes as effectively as possible; • Monitor and propose improvements in the area, targeting process enhancements related to activities, projects and initiatives to incorporate industry best practices.