Senior Manager - Data Science (Forecasting)

We are seeking an experienced Senior Manager - Data Science (Forecasting) to lead the design, development, and deployment of advanced forecasting solutions that drive strategic business decisions. This role combines deep expertise in time-series forecasting, machine learning, and statistical modeling with strong people leadership, program delivery, stakeholder management, and business strategy. The ideal candidate is a hands-on leader who can guide teams, influence business outcomes, and deliver scalable forecasting solutions across multiple domains.

Technical Leadership & Forecasting Excellence

• Lead the development and deployment of forecasting models for demand, capacity, revenue, workforce, supply chain, or other business planning functions.
• Design and implement advanced forecasting techniques including:
• ARIMA/SARIMA
• Exponential Smoothing (ETS)
• Prophet
• Machine Learning and Deep Learning-based forecasting models
• Ensemble forecasting approaches
• Drive model accuracy improvements through feature engineering, model optimization, and continuous monitoring.
• Establish forecasting best practices, frameworks, and governance standards.
• Evaluate emerging technologies and techniques in forecasting, predictive analytics, AI, and Generative AI.

Hands-on Data Science & Delivery

• Remain actively involved in solution design, model reviews, architecture discussions, and critical technical challenges.
• Lead end-to-end data science project delivery from problem definition through production deployment.
• Work closely with engineering teams to operationalize forecasting models at scale.
• Ensure solutions meet business, performance, reliability, and scalability requirements.
• Drive adoption of MLOps and model lifecycle management practices.

People Leadership

• Build, mentor, and manage a high-performing team of data scientists, ML engineers, and analysts.
• Conduct performance reviews, career development planning, and coaching.
• Foster a culture of innovation, ownership, accountability, and continuous learning.
• Guide team members on technical excellence, stakeholder management, and business impact.

Business Partnership & Stakeholder Management

• Collaborate with business leaders to understand forecasting requirements and translate them into analytical solutions.
• Present insights, forecasts, assumptions, risks, and recommendations to senior leadership.
• Influence strategic planning through data-driven decision-making.
• Partner with product, engineering, operations, finance, and business teams to align forecasting initiatives with organizational goals.

Strategy & Innovation

• Define and execute the forecasting roadmap aligned with business objectives.
• Identify opportunities to leverage AI, ML, and GenAI for forecasting automation and decision support.
• Measure and communicate business impact of forecasting initiatives.
• Drive thought leadership within the organization on predictive analytics and forecasting capabilities.

Required Qualifications

• Master's or Ph.D. in Data Science, Statistics, Mathematics, Operations Research, Economics, Computer Science, or related discipline.
• 12+ years of experience in Data Science, Analytics, Machine Learning, or Forecasting.
• 3+ years of experience leading data science teams.
• Strong expertise in time-series forecasting and predictive modeling.
• Proficiency in Python, SQL, and data science ecosystems.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Experience deploying machine learning solutions into production environments.
• Strong communication and stakeholder management skills.
• Proven ability to lead multiple initiatives and influence executive stakeholders.

Preferred Qualifications

• Experience in demand forecasting, workforce forecasting, financial forecasting, retail, supply chain, or operations analytics.
• Exposure to Generative AI and Large Language Models.
• Experience with MLOps frameworks and model monitoring.
• Familiarity with Databricks, Spark, Azure Machine Learning, or similar platforms.
• Experience managing teams of 5-15+ data scientists and ML engineers.