SATIC PwC: Data Science Manager (Revenue Growth Management) at PricewaterhouseCoopers (PwC)

• We are looking for candidates who bring in-depth market experience, who see technology as a way to enable and drive human innovation and who want to be a part of a community of solvers, to tackle the biggest challenges in society.
• If you are looking for a career where every day is different, where challenges are complex and where you can make a real difference, then we want to hear from you. By choosing to join SATIC, you are choosing to work with teams all over the world, harnessing the power of world leading tech and making a difference to real peoples lives.

Summary:

• The Revenue Growth Management Data Science Manager will lead the development and application of advanced analytics, machine learning, and commercial modelling to support revenue growth, pricing, promotion, assortment, and customer investment decisions.
• The role will combine strong data science expertise with commercial and strategic insight to help clients and business stakeholders identify growth opportunities, improve decision-making, and optimise revenue and profitability.
• The successful candidate will work closely with commercial, sales, marketing, finance, category management, data engineering, and technology teams to translate complex data into practical recommendations and scalable analytical solutions.

Responsibilities of role:

Revenue Growth Management Analytics

• Lead advanced analytics across the core Revenue Growth Management levers, including pricing, promotions, product mix, assortment, trade investment, customer segmentation, and pack-price architecture.
• Develop analytical models to assess price elasticity, promotional effectiveness, cannibalisation, incrementality, demand shifts, and margin impact.
• Identify revenue, volume, market-share, and profitability opportunities across products, channels, customers, and markets.
• Support the development of pricing and promotional strategies based on consumer behaviour, competitor activity, cost movements, and commercial objectives.
• Evaluate trade investment and promotional spend to improve return on investment and reduce ineffective expenditure.
• Develop scenario models and simulations to assess the impact of pricing, promotion, assortment, and portfolio decisions.
• Translate analytical findings into clear recommendations for commercial and executive stakeholders.

Data Science and Advanced Modelling

• Design, build, validate, and deploy statistical and machine-learning models that support commercial decision-making.
• Apply techniques such as regression, forecasting, clustering, classification, optimisation, causal inference, time-series analysis, and predictive modelling.
• Develop demand forecasting and price-elasticity models using internal and external datasets.
• Build customer, shopper, product, and channel segmentation models to identify differentiated growth opportunities.
• Develop optimisation models for pricing, promotions, product assortment, and commercial investment.
• Ensure models are explainable, robust, scalable, and aligned with business requirements.
• Monitor model performance and establish processes for recalibration, maintenance, and continuous improvement.

Data Management and Analytical Solutions

• Work with data engineering and technology teams to identify, integrate, and prepare data from multiple internal and external sources.
• Assess data quality, completeness, consistency, and suitability for analytical use.
• Define data requirements for RGM models, dashboards, decision-support tools, and reporting solutions.
• Support the development of reusable analytical products, accelerators, and data pipelines.
• Ensure appropriate controls are applied to data security, privacy, governance, and regulatory compliance.
• Document modelling assumptions, methodologies, limitations, and data lineage.
• Support the industrialisation and deployment of analytical models into business processes and technology platforms.

Commercial Insight and Decision Support

• Partner with sales, marketing, finance, category, and commercial teams to understand strategic priorities and decision-making needs.
• Convert complex analysis into practical commercial actions, supported by clear financial and operational implications.
• Develop executive-level dashboards, visualisations, and decision-support tools.
• Present insights, scenarios, and recommendations to senior leadership and client stakeholders.
• Challenge existing commercial assumptions using evidence-based analysis.
• Support annual planning, revenue forecasting, portfolio reviews, customer negotiations, and investment decisions.
• Track the business impact of implemented recommendations and identify opportunities for further value creation.

Project and Delivery Management

• Lead data science and RGM workstreams from problem definition through analysis, solution development, implementation, and benefits realisation.
• Define project scope, analytical approaches, milestones, resource requirements, risks, and deliverables.
• Manage multiple projects or workstreams across different clients, business units, categories, or geographies.
• Ensure analytical outputs are delivered within agreed timelines, budgets, quality standards, and governance requirements.
• Coordinate activities across data scientists, analysts, business stakeholders, engineers, and external partners.
• Manage risks, dependencies, assumptions, and changes in project scope.
• Provide regular progress, value, and risk reporting to programme and business leadership.
• Stakeholder and Client Management
• Build trusted relationships with senior commercial, finance, marketing, sales, data, and technology stakeholders.
• Act as a trusted advisor on the use of data science within Revenue Growth Management.
• Facilitate workshops to define commercial problems, analytical requirements, and implementation priorities.
• Communicate technical concepts in clear business language for non-technical audiences.
• Manage stakeholder expectations regarding analytical outcomes, data limitations, timelines, and model accuracy.
• Support clients and business teams in adopting analytical recommendations and embedding them into decision-making processes.

Team Leadership and Capability Development

• Lead, coach, and mentor data scientists, analysts, and consultants.
• Review and quality-assure analytical approaches, code, models, and deliverables.
• Allocate work, manage team capacity, and monitor performance across workstreams.
• Support recruitment, onboarding, performance management, and professional development.
• Build capability in RGM analytics, commercial modelling, machine learning, and data visualisation.
• Promote a culture of experimentation, collaboration, technical excellence, and commercial impact.
• Develop reusable frameworks, methodologies, templates, and analytical assets.
• Commercial and Practice Contribution
• Support the development of proposals, business cases, statements of work, and client presentations.
• Contribute to effort estimation, resource planning, financial management, and project forecasting.
• Identify opportunities to expand client engagements and introduce additional analytics or RGM services.
• Contribute to thought leadership, market insights, industry points of view, and capability development.
• Support the growth and development of the organisation’s Revenue Growth Management and Data Science offering.
• Maintain awareness of emerging analytical methods, technologies, and industry practices.

Desirable skill sets include:

Technical Skills

• Advanced proficiency in Python, R, SQL, or similar analytical programming languages.
• Strong experience with statistical modelling, machine learning, forecasting, optimisation, and experimentation.
• Experience using data science libraries and frameworks such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, or equivalent technologies.
• Experience with visualisation and reporting tools such as Power BI, Tableau, or similar platforms.
• Working knowledge of cloud data and analytics platforms such as Azure, AWS, Google Cloud, Databricks, or Snowflake.
• Experience working with structured and unstructured commercial datasets, including sales, pricing, promotional, customer, product, market, and competitor data.
• Understanding of data engineering, data pipelines, APIs, model deployment, and machine-learning operations would be advantageous.
• Knowledge of version control, reproducible analytics, and collaborative development practices.
• Strong proficiency in Excel and financial or commercial modelling.

Revenue Growth Management Knowledge

The successful candidate should demonstrate a strong understanding of:

• Pricing strategy and price elasticity
• Promotional effectiveness and incrementality
• Trade investment and promotional return on investment
• Customer and channel profitability
• Product and customer segmentation
• Pack-price architecture
• Portfolio and product-mix optimisation
• Assortment optimisation
• Demand forecasting
• Revenue and margin management
• Consumer, shopper, and competitor behaviour
• Commercial planning and performance measurement