Junior Data Scientist

Hypersonix Inc.; India; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

Hypersonix Inc. is seeking a dedicated professional to support the development of data-driven features, focusing on pricing and promotion strategies to enhance revenue. This role involves building and maintaining analytical models for demand forecasting and applying data science techniques to retail business challenges.

The ideal candidate should have solid experience in Python, SQL, and MLOps, with a foundation in mathematics. Excellent communication skills are essential for conveying findings effectively.

Qualifications

• Strong foundation in mathematics including linear algebra, probability & statistics.
• Hands-on experience with Python (pandas, NumPy, OOP), and scikit-learn or similar libraries.
• Experience in data analysis using SQL and Python including data extraction and preparation.
• Experience in retail pricing strategies and demand forecasting.
• Familiarity with MLOps and CI/CD pipelines.
• Exceptional communication and presentation skills.

Responsibilities

• Support the development of data-driven features for pricing and promotion strategies.
• Build, test, and maintain analytical models for demand forecasting and price sensitivity.
• Apply data science and machine learning techniques to solve retail business problems.
• Analyze retail data to identify trends and generate actionable insights.
• Collaborate with teams to implement and scale data solutions.

Skills

Mathematics

Python

SQL

Machine Learning

Data Analysis

Data Visualization

Communication Skills

Tools

scikit-learn

Databricks

Google Cloud Vertex AI

Amazon SageMaker

Azure Machine Learning

Job description

At Hypersonix, we are building the leading enterprise Generative AI Platform for Commerce. Our Flagship Product – Competitor + Pricing AI – scrapes the product catalogue for our enterprise customers and their competitors, and uses RAG to identify the nearest competitive match for each of our customer's product. In addition, we forecast the demand and price elasticity of each of our customer's products to facilitate intelligent pricing strategies that were previously impossible to achieve.

About the role

• 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

Qualifications

• Strong foundation in mathematics including linear algebra, probability & statistics
• Solid understanding of hypothesis testing, regression analysis, and core machine learning algorithms, with at least 2 years of hands-on experience with Python(pandas, NumPy, OOP), as well as with scikit-learn or similar machine/Deep learning open-source software libraries
• 2+ years of experience in data analysis using SQL and Python, including data extraction, data preparation, EDA, and basic statistical analysis
• 1+ years of experience in retail or CPG, with proven expertise in price elasticity modeling, demand forecasting, promotion optimization, pricing and other retail strategies
• 1+ year of experience with MLOps (Databricks, Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning, or similar), and CI / CD pipelines
• 1+ year of experience in developing, integrating, delivering, and supporting AI and analytics solutions, with exposure to pricing optimization and retail analytics
• Exceptional communication and presentation skills, capable of conveying complex insights clearly and effectively to both technical and non-technical audiences