Associate Principal/Principal- Forecasting

Job description

PharmaACE has been a thought leader and go-to partner for major pharmaceutical companies, supporting their commercial forecasting needs for over two decades. Our deep expertise spans Forecasting, Business Analytics, Competitive Intelligence, Sales Analytics, and operating through an Analytics Centre of Excellence

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With the evolving market landscape, we are building the next generation of forecasting solutions by leveraging Generative AI. If youve mastered traditional forecasting models and are eager to expand into new-age skill sets including Python, prompt engineering, building data pipelines, and designing agentic workflows this is your opportunity.

You will play a pivotal role in creating GenAI-powered forecasting use cases and applying them in day-to-day forecasting work.

Lets work together to make Forecasting truly ForecAstIng

Leadership:

• Act as the primary client contact for key AI/analytics engagements, ensuring alignment between project deliverables and strategic business needs.
• Facilitate client workshops, deliver strategic recommendations, and manage expectations across multiple stakeholders.
• Present AI-enabled insights and model outputs through compelling narratives and data visualizations tailored for business leaders.
• Stay up to date with the new technological advancement in the forecasting space and being a thought partner for our clients to drive innovative solution in their respective organization
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  Develop deep client relationships to identify follow-on opportunities and contribute to account growth.

Role & responsibilities

Development and Implementation of Forecasting AI Solution:

• Lead end-to-end delivery of applied AI and analytics driven forecasting projects, combining pharmaceutical domain knowledge with advanced data science techniques to deliver actionable insights.
• Drive design and implementation of ML-based forecasting models, market sizing tools, and other AI solutions for commercial strategy, brand planning, and lifecycle management.
• Translate business challenges into AI/ML use cases and oversee development and deployment of appropriate models (e.g., time-series, clustering, classification, NLP).
• Integrate and analyze primary research, secondary data, and third-party sources (e.g., IQVIA, DRG, RWD) to inform strategic recommendations.
• Conduct robust market analysis leveraging AI-based tools (e.g., LLMs, ML-based forecasting models) to provide competitive intelligence and recommendations on brand planning, lifecycle management, and launch strategies.
• Collaborate with cross-functional teams (data scientists, engineers, consultants) to ensure technical robustness and business relevance of all solutions.
• Develop and refine Excel-based and code-based (Python/R) forecasting models, and transform them into client facing tools and dashboards using platforms like Power BI or Tableau.
• Continuously monitor and increase the accuracy of the forecast models.
• Leverage XAI (explainable AI) methodologies to ensure models are interpretable, trustworthy, and aligned with client regulatory frameworks.

Team Leadership & Capability Building:-

• Manage and mentor teams of Consultants, Senior Consultants, and Analysts, guiding technical execution and business storytelling.
• Contribute to the development of internal AI/analytics capabilities by codifying best practices, creating reusable frameworks, and enabling knowledge sharing.
• Support the training and development of junior team members on AI/ML tools, pharma analytics, and client engagement.
• Promote a learning culture that embraces innovation, experimentation, and continuous improvement.

Preferred candidate profile

• Bachelors in Life Sciences, Engineering, Computer Science, or related field; Masters in Biotech, Data Science, Business Analytics, or MBA preferred.
• Minimum 12 years of experience in commercial pharma analytics or consulting, with at least 35 years in hands-on AI/ML application.
• Proven track record of managing client engagements and delivering analytics or AI-based solutions in the life sciences domain.
• Experience with Python, R, SQL, Power BI/Tableau, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch) will be preferred.
• Strong understanding of pharmaceutical datasets (IQVIA, DRG, RWE, EMR/EHR) and how to integrate them into AI solutions.
• Experience with global pharma markets (US/EU) highly desirable.