Pricing Data Scientist

Job Description

Summary

As a Data Scientist, Pricing , you will be pivotal in transforming large datasets into actionable analytics and optimizing the company's pricing guidance platform. Your primary goal is to drive annual margin improvements . You will collaborate with Business Development Category leaders and Sales and Services leaders to develop and implement the governance, policies, and processes for solution pricing to maximize business value, sales, and profitability.

Key Responsibilities

The role focuses on three main areas : Pricing Strategy & Analytics, Modeling & Optimization, and Data & ML Engineering.

1. Pricing Strategy & Analytics

Model Management & Communication : Manage the development of robust commercial pricing models and clearly present inputs and outputs to senior leaders and stakeholders to drive decision-making.

Segmentation & Margin : Maintain the Pricing Segmentation model accuracy to drive Seller confidence in recommendations, thereby increasing margins and profits.

Actionable Insights : Develop quantitative, actionable insights through the application of statistical and / or advanced data analytics techniques.

Competitive Intelligence : Gather and maintain competitive intelligence from various sources in a standardized manner to inform market benchmarking and competitive actions.

Governance & Policy : Validate and enhance current pricing governance, policies, rules, and processes (e.g., pricing floors / ceilings, approvals) and continually improve them within systems.

Forecasting & Metrics : Forecast the impact of pricing decisions on the overall business and communicate expectations, profitability, and key pricing metrics.

Market Awareness : Stay current with market conditions to develop and refine pricing strategies and models for competitive positioning.

2. Modeling & Optimization

Complex Problem Solving : Solve complex problems using advanced mathematical modeling and optimization techniques, including big data pre-processing, problem formulation, feature engineering, algorithmic selection and evaluation, and hyperparameter tuning for machine learning.

Project Contribution : Contribute to all stages of data science projects, from raw data mining to translating complex technical topics into business solutions.

3. Data & ML Engineering

System Design : Design and implement scalable machine learning systems and infrastructure .

Data Pipeline Optimization : Optimize data pipelines for preprocessing, feature engineering, and model training.

Model Integration : Collaborate with DevOps teams to ensure seamless integration of Machine Learning models into production.

Data Models : Maintain and enhance a set of critical data models supporting business use cases.

Technology Advancement : Stay updated with the latest advancements in machine learning, DevOps, and cloud technologies.

Requirements

Experience & Education

5+ years of experience in pricing strategy and implementation.

Post-secondary Degree or Diploma . An MBA is considered an asset.

Must be proficient in building and deploying models , not just extracting insights.

Experience working on larger deals / complex transactions .

Technical Proficiency

Programming & ML : Proficiency in Python, R, SQL and experience with ML libraries and frameworks like Scikit-learn, NumPy , etc.

Data Engineering : Experience engineering information out of massive, complex, and, in some cases, unstructured datasets.

Visualization : Proficiency in one or more visualization tools like Tableau, Power BI , etc.

Core Competencies

Analytical : Strong problem-solving ability and analytical thinking.

Communication : Strong written, verbal, and interpersonal communication skills; ability to effectively communicate at all levels in the organization.

Stakeholder Management : Ability to influence, collaborate, and interact effectively with multiple key stakeholders across several functional areas to align on decisions.

Business Acumen : Ability to apply a strong business sense with technical skills to effectively balance decisions around the complexity and speed of project delivery.

Work Style : Detailed oriented; ability to work successfully in a project team environment; ability to handle sensitive and confidential situations with diplomacy.

Benefits

WHAT WE OFFER :

• Great Place to Work-Certified Company
• Premium HMO
• Holistic employee experience
• Work-from-home and hybrid work setup
• Rewards and incentives
• Monthly engagement activities
• Career advancement opportunities
• Paid referral program

Requirements

Programming & ML : Proficiency in Python, R, and SQL. Experience with ML libraries and frameworks (e.g., Scikit-learn, NumPy). Data Engineering : Experience engineering information out of massive, complex, and, in some cases, unstructured datasets. Visualization : Proficiency in one or more visualization tools (e.g., Tableau, Power BI).