Decision Scientist

Required Qualifications

• 3-7 years exp, should be very strong hands-on programming experience in languages such as Python, Java SQL. BQL (Biq Query Language) is a plus
• Automation framework/tool experiences in TDD, BDD, Pytest, Playwright.
• 1-2 years Technical hands-on experience of Azure/GCP application development and Data Integrations.
• Technical knowledge at novice level on Gen AI, MLOPs is a plus.
• Ability to understand test data setup and understanding of data components.
• 1-2 years of hands-on Azure Databricks experiences for Data engineering, Dashboard creation. 
• Ability to write simple to very complex SQL scripts.
• Build and executes test plans and test scripts, composes simple and moderately complex SQL scripts, and develops basic automation tools to streamline cloud infrastructure management and deployment 
• Bachelor of Science degree in computer science, engineering or other related field that provides similar skills.

Preferred Qualifications

• Demonstrates self-motivation, good judgment, and active listening skills.
• Strong communication skills (verbal, technical and written).
• Detail oriented and organized.

Strong commitment and dedication to the position and a team player.

FAQ

1. What are the core responsibilities of a Decision Scientist?
A Decision Scientist focuses on using data, statistical models, and business context to drive high-impact decisions. The role involves analyzing complex datasets, building predictive models, and translating insights into actionable recommendations. It bridges the gap between data science and business strategy.

2. How does a Decision Scientist differ from a Data Scientist?
While both roles use data and modeling techniques, a Decision Scientist places greater emphasis on business outcomes and decision-making frameworks. The role prioritizes interpretability, scenario analysis, and actionable insights. It often involves closer collaboration with business stakeholders.

3. What types of problems does this role typically solve?
Common problems include forecasting, pricing optimization, customer segmentation, and risk analysis. The role may also support strategic planning and operational decision-making. Solutions are designed to directly influence business performance.

4. What tools and technologies are commonly used in this role?
Common tools include Python, R, SQL, and data visualization platforms like Tableau or Power BI. Statistical modeling, machine learning libraries, and experimentation platforms are also used. Cloud data platforms may support large-scale analysis.

5. How is data used to support decision-making in this role?
Data is used to identify trends, test hypotheses, and evaluate different scenarios. The Decision Scientist builds models and frameworks that quantify trade-offs and outcomes. Insights are communicated clearly to guide business decisions.