Key Facts

Remote From:

Anywhere

Full time

Junior (1-2 years)

English

Hard Skills

Other Skills

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Presentations
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Collaboration
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Communication

Roles & Responsibilities

Strong foundation in mathematics including linear algebra, probability, and statistics.
2+ years hands-on Python (pandas, NumPy, OOP) experience and proficiency with scikit-learn or similar ML libraries.
2+ years data analysis using SQL and Python (data extraction, EDA, and basic statistical analysis), with experience in retail/CPG domains (price elasticity, demand forecasting, promotion optimization).
1+ year of MLOps experience (Databricks, Vertex AI, SageMaker, Azure ML or similar) and CI/CD pipelines, with a track record delivering AI/analytics solutions in pricing/retail.

Requirements:

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 elasticity, 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.
Collaborate with cross-functional teams to implement and scale data solutions, and communicate findings through clear analysis and visualizations.

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.

We are seeking a hands-on Senior Data Scientist with proven experience in retail, CPG, and/or E-Commerce, who can lead the Machine Learning portion of our stack: demand forecasting, price elasticity, and pricing / promotion strategy.

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.