Decision Scientist (The Strategic Data Advisor)

About the position

The Decision Scientist, also known as The Strategic Data Advisor, plays a crucial role in transforming data into actionable insights that drive business decisions. This position involves developing analytical models, collaborating with various teams, and utilizing statistical and machine learning techniques to optimize performance and support strategic initiatives across the organization.

Responsibilities

• Develop analytical models for decision-making by creating statistical and machine learning models to analyze business performance and predict trends.
• Translate data into actionable insights through in-depth analyses to identify key trends, opportunities, and challenges.
• Collaborate on strategic planning and optimization by working closely with cross-functional teams to align insights with business goals.
• Design and implement experiments, including A/B testing, to measure the impact of different strategies and provide recommendations based on empirical evidence.
• Create and present data visualizations and reports that effectively communicate findings and recommendations to stakeholders.
• Optimize decision-making frameworks by establishing best practices for data-driven decision-making across the organization.
• Stay updated on analytical tools and techniques to integrate new methodologies that improve model accuracy and business relevance.

Requirements

• Strong background in statistical analysis, machine learning, and decision science techniques, including regression analysis, clustering, and predictive modeling.
• Proficiency in programming languages such as Python, R, or SQL for data analysis and model development, with familiarity in data visualization tools like Tableau or Power BI.
• Experience in designing and interpreting experiments, including A/B tests, to evaluate the effectiveness of business strategies.
• Ability to translate complex data into business insights, with an understanding of key performance indicators and business metrics that impact organizational success.
• Excellent communication skills to present insights clearly to both technical and non-technical audiences.

Nice-to-haves

• Certifications in data science or analytics (e.g., Certified Analytics Professional, Google Data Analytics Professional) are advantageous.
• Background in a specific industry, such as e-commerce, finance, or healthcare, is beneficial but not required.

Benefits

• Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Competitive vacation, sick leave, and 20 paid holidays per year.
• Flexible work schedules and telecommuting options.
• Opportunities for training, certification reimbursement, and career advancement programs.
• Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life insurance and short-term/long-term disability coverage.
• Confidential counseling and support services for personal and professional challenges.
• Financial assistance for continuing education and professional development.
• Opportunities to participate in community service and volunteer activities.
• Employee recognition programs to celebrate achievements and milestones.