Decision Science Manager

Cinemark•Plano, TX

About The Position

The Decision Science Manager solves high-impact business challenges across Cinemark by combining advanced analytics, decision science, experimentation, machine learning, optimization, and business strategy. This role partners with leaders to frame ambiguous problems, evaluate alternatives, and deliver measurable outcomes that improve customer experience, operational performance, and enterprise growth. The ideal candidate is a technically strong, business-minded problem solver who applies the right methods to the right questions and measures success by decision quality, adoption, and value creation—not model development alone.

Requirements

Bachelor’s degree in computer science, Engineering, a quantitative field, technical discipline, or business-related field; master’s degree preferred.
7+ years of experience in decision science, data science, analytics, strategy, consulting, or a related field.
Proven track record of solving complex business problems and driving measurable results.
Strong proficiency in SQL, Python, statistical modeling, predictive modeling, and analytical methodologies.
Experience building dashboards and data visualizations in Power BI, including working with high-cardinality data.
Familiarity with Microsoft Azure, Microsoft Fabric, and modern data platforms.
Strong business acumen, stakeholder management, executive storytelling, and communication skills.
Ability to clearly explain analytical approaches, model assumptions, results, and implications to technical and non-technical stakeholders.
Strong attention to detail and ability to manage multiple competing priorities in a fast-paced environment.

Responsibilities

Lead end-to-end problem solving for strategic and operational business challenges across the enterprise.
Translate ambiguous business questions into analytical frameworks, hypotheses, and actionable recommendations.
Apply statistical analysis, predictive modeling, machine learning, optimization, simulation, forecasting, and causal inference to improve decision-making.
Design experiments, A/B tests, and measurement frameworks to validate initiatives and quantify business impact.
Build scalable models, dashboards, and decision-support tools that help teams act with confidence.
Partner with cross-functional leaders to identify opportunities, evaluate tradeoffs, and support execution.
Leverage customer, operational, financial, and market data to uncover insights that inform enterprise decisions.
Mentor analysts and data scientists in analytical rigor, technical problem-solving, and business application.