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 is the primary role of a Decision Scientist?
A Decision Scientist focuses on using data, analytics, and modeling to support strategic business decisions. The role involves translating complex data into actionable insights that guide leadership and operational teams. It bridges the gap between data science and business strategy.

2. How does a Decision Scientist differ from a Data Scientist?
While both roles work with data, a Decision Scientist places greater emphasis on business context and decision-making. The role focuses on interpreting results, influencing stakeholders, and driving outcomes rather than just building models. Communication and strategic thinking are key differentiators.

3. What types of problems does a Decision Scientist typically solve?
Problems often include forecasting, optimization, customer behavior analysis, and operational efficiency improvements. The role addresses ambiguous business questions and converts them into structured analytical approaches. Solutions are designed to directly impact business performance.

4. What tools and technologies are commonly used in this role?
Common tools include SQL, Python or R for analysis, and visualization platforms like Tableau or Power BI. Statistical and machine learning libraries are used for modeling and forecasting. Data platforms and cloud environments may also be part of the tech stack.

5. How does this role collaborate with stakeholders?
The Decision Scientist works closely with business leaders, product teams, and data engineers. They gather requirements, align on objectives, and present insights in a clear and actionable manner. Strong collaboration ensures data-driven decisions are effectively implemented.