Job Description

1) Predictive Modelling & Business Forecasting

•       Design, build, and deploy predictive models and forecasting pipelines for core business operations: revenue forecasting, project profitability prediction, resource utilization optimization, and cost modelling.
•       Define model objectives, success metrics, and validation frameworks aligned with business KPIs.
•       Translate complex model outputs into clear, actionable business recommendations for leadership and cross-functional teams.
•       Continuously improve model accuracy and relevance through monitoring, retraining, and feedback loops.

2) Data Modelling & Feature Engineering

•       Architect data models and feature stores that support scalable machine learning and analytics use cases.
•       Work with BI and Engineering teams to ensure clean, reliable, and well-documented data pipelines feed into modelling workflows.
•       Define and maintain metric definitions, data dictionaries, and source-of-truth standards for predictive use cases.
•       Perform data profiling, anomaly detection, and quality validation to ensure model inputs are robust.

3) AI-First Strategy & Use Case Development

•       Partner with CTO and business leaders to identify and prioritize high-impact AI/ML use cases across operations, marketing performance, and internal platform capabilities.
•       Build the roadmap for embedding prediction and intelligent automation into PMAX’s workflows and tools.
•       Evaluate and recommend emerging technologies (LLMs, GenAI, AutoML) where they create genuine business value — not innovation for its own sake.
•       Establish experimentation frameworks (A/B testing, offline evaluation, champion-challenger) to validate model impact before production deployment.

4) Team Building & Data Science Practice Leadership

•       Recruit, coach, and manage a team of 2–4 Data Scientists and/or ML Engineers.
•       Set quality standards for the DS practice: code review, model documentation, reproducibility, and deployment discipline.
•       Foster a culture of rigour, curiosity, and business-first thinking within the team.
•       Define career development paths and mentoring practices for team members.

5) Cross-Functional Collaboration & Stakeholder Enablement

•       Work closely with BI to ensure predictive insights are surfaced in dashboards and decision workflows (not siloed in notebooks).
•       Partner with Engineering to productionize models — ensuring reliability, monitoring, and scalability.
•       Collaborate with Finance, Operations, Account, and Media teams to deeply understand their planning and decision-making needs.

•       Present findings and recommendations in clear, business-oriented language to non-technical stakeholders and senior leadership.